title

Python For Data Science Full Course - 9 Hours | Data Science With Python | Python Training | Edureka

description

๐ฅEdureka Python for Data Science Certification Training (Use Code "๐๐๐๐๐๐๐๐๐"): https://www.edureka.co/data-science-python-certification-course
This Edureka video on the 'Python For Data Science Full Course' will help you learn Python for Data Science including all the relevant libraries. Following are the topics discussed in this Python for Data Science tutorial:
00:00 Agenda
02:37 Introduction To Data Science
03:03 Need for Data Science
06:05 What is Data Science
10:56 Data Science Life Cycle
26:50 Jupyter Notebook Tutorial
48:34 Statistics For Data Science
02:14:32 Python Libraries For Data Science
02:14:25 Python Numpy
02:44:42 Python Pandas
03:18:24 Python Scipy
03:36:21 Python Matplotlib
04:04:19 Python Seaborn
04:22:57 Machine Learning With Python
04:33:47 Maths For Machine Learning
05:32:40 Machine Learning Algorithms
05:41:36 Classification In Machine Learning
06:14:07 Linear Regression In Machine Learning
06:41:59 Logistic Regression In Machine Learning
7:33:44 Deep Learning With Python
07:40:10 Keras Tutorial
08:06:14 TensorFlow Tutorial
08:52:35 Pyspark Tutorial
๐น Edureka Community: https://bit.ly/EdurekaCommunity
๐น Python Data Science Tutorial Playlist: https://goo.gl/WsBpKe
๐น Python Data Science Blog Series: http://bit.ly/2sqmP4s
๐ด ๐๐๐ฎ๐ซ๐๐ค๐ ๐๐ง๐ฅ๐ข๐ง๐ ๐๐ซ๐๐ข๐ง๐ข๐ง๐ ๐๐ง๐ ๐๐๐ซ๐ญ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง๐ฌ
๐ต Python Online Training: http://bit.ly/3Oubt8M
๐ต Data Science Online Training: http://bit.ly/3V3nLrc
๐ด ๐๐๐ฎ๐ซ๐๐ค๐ ๐๐จ๐ฅ๐-๐๐๐ฌ๐๐ ๐๐จ๐ฎ๐ซ๐ฌ๐๐ฌ
๐ต Data Scientist Masters Program: http://bit.ly/3tUAOiT
๐ต Python Developer Masters Program: http://bit.ly/3EV6kDv
๐ด ๐๐๐ฎ๐ซ๐๐ค๐ ๐๐ง๐ข๐ฏ๐๐ซ๐ฌ๐ข๐ญ๐ฒ ๐๐ซ๐จ๐ ๐ซ๐๐ฆ๐ฌ
๐ต Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh
๐ Artificial and Machine Learning PGD with E&ICT Academy
NIT Warangal: http://bit.ly/3OuZ3xs
๐ด Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV
---------------------------------
Edureka Community: https://bit.ly/EdurekaCommunity
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
SlideShare: https://www.slideshare.net/EdurekaIN
#edureka #PythonEdureka #pythonfordatasciencefullcourse #pythonprojects #pythontutorial #PythonTraining
About the course
Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. For over a decade, Python has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing.
Edureka's Python Certification Training not only focuses on the fundamentals of Python, Statistics and Machine Learning but also helps one gain expertise in applied Data Science at scale using Python. The training is a step by step guide to Python and Data Science with extensive hands-on. The course is packed with several activity problems and assignments and scenarios that help you gain practical experience in addressing predictive modeling problems that would either require Machine Learning using Python. Starting from basics of Statistics such as mean, median and mode to exploring features such as Data Analysis, Regression, Classification, Clustering, Naive Bayes, Cross-Validation, Label Encoding, Random Forests, Decision Trees and Support Vector Machines with a supporting example and exercise help you get into the weeds.
Furthermore, you will be taught of Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to train your machine based on real-life scenarios using Machine Learning Algorithms.
Edurekaโs Python course will also cover both basic and advanced concepts of Python like writing Python scripts, sequence and file operations in Python. You will use libraries like pandas, numpy, matplotlib, scikit, and master concepts like Python machine learning, scripts, and sequence.
---------------------------------
Why learn Python?
It's continued to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built-in debugger.
---------------------------------
For more information, please write back to us at sales@edureka.in or call us at IND: 9606058406 / US: 18338555775

detail

{'title': 'Python For Data Science Full Course - 9 Hours | Data Science With Python | Python Training | Edureka', 'heatmap': [{'end': 5072.281, 'start': 4723.522, 'weight': 1}], 'summary': "This 9-hour python for data science course covers python's significance in data science, data manipulation, analysis, visualization, jupyter notebook setup, statistics, probability, numpy, pandas, data analysis, visualization, machine learning mathematics, linear algebra, differentiation, machine learning problem-solving, building predictors and classifier models, titanic data analysis, logistic regression, deep learning, keras, wine data analysis, price prediction, tensorflow basics, model optimization, neville mine identifier model, pyspark, and mllib.", 'chapters': [{'end': 616.875, 'segs': [{'end': 53.808, 'src': 'embed', 'start': 25.396, 'weight': 0, 'content': [{'end': 31.559, 'text': "It's art of deriving insights and trends in data in order to solve real-world complex data-driven problems.", 'start': 25.396, 'duration': 6.163}, {'end': 38.561, 'text': 'Since Python undoubtedly tops the list of most preferred programming languages with its out-of-box features.', 'start': 32.259, 'duration': 6.302}, {'end': 40.702, 'text': 'data science has become a lot easier with Python.', 'start': 38.561, 'duration': 2.141}, {'end': 47.005, 'text': 'This complete crash course consists of everything you need to know to get started with data science using Python.', 'start': 41.463, 'duration': 5.542}, {'end': 53.808, 'text': 'To make it much more easier for you guys to learn as well as understand, we have divided this entire course into six modules.', 'start': 47.725, 'duration': 6.083}], 'summary': 'Python simplifies data science, a top programming language, divided into six modules.', 'duration': 28.412, 'max_score': 25.396, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY25396.jpg'}, {'end': 114.732, 'src': 'embed', 'start': 90.473, 'weight': 1, 'content': [{'end': 99.14, 'text': 'This module covers all relevant libraries of Python for data science lifecycle processes like data exploration, data mining, data cleaning,', 'start': 90.473, 'duration': 8.667}, {'end': 100.842, 'text': 'data visualization and many more.', 'start': 99.14, 'duration': 1.702}, {'end': 103.704, 'text': 'Next module is Machine Learning with Python.', 'start': 101.602, 'duration': 2.102}, {'end': 110.869, 'text': "In this module, we'll more to more advanced concepts of data science like model building, which is where machine learning comes into picture.", 'start': 104.244, 'duration': 6.625}, {'end': 114.732, 'text': 'Here, we deep dive into basics of machine learning with Python.', 'start': 111.369, 'duration': 3.363}], 'summary': 'Python module covers data science libraries for exploration, mining, cleaning, and visualization. next, machine learning module focuses on advanced concepts and model building.', 'duration': 24.259, 'max_score': 90.473, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY90473.jpg'}, {'end': 222.635, 'src': 'embed', 'start': 194.669, 'weight': 2, 'content': [{'end': 198.071, 'text': 'But today most of the data is unstructured or you can say semi-structured.', 'start': 194.669, 'duration': 3.402}, {'end': 203.373, 'text': 'So by these terms, I mean the data is an unorganized format and a lot of data is being generated.', 'start': 198.491, 'duration': 4.882}, {'end': 207.795, 'text': "Let's say from a multimedia your text files your sensors and even from your instrument.", 'start': 203.533, 'duration': 4.262}, {'end': 212.768, 'text': "So if you look at today's time the data is actually getting created faster than you could ever imagine.", 'start': 208.305, 'duration': 4.463}, {'end': 216.791, 'text': "So let's say you are traveling on road and you're using Google Maps for navigation.", 'start': 213.389, 'duration': 3.402}, {'end': 222.635, 'text': 'So there is a lot of data being captured through satellite and it is transmitted real-time through your handle devices.', 'start': 217.171, 'duration': 5.464}], 'summary': "Most data today is unstructured, and it's being generated at an unprecedented pace, including from multimedia, sensors, and instruments.", 'duration': 27.966, 'max_score': 194.669, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY194669.jpg'}, {'end': 271.409, 'src': 'embed', 'start': 247.348, 'weight': 3, 'content': [{'end': 256.736, 'text': 'But now what data science has enabled us to do is that we are moving from an approach wherein we have the data which tells us the situation or what exactly the situation was,', 'start': 247.348, 'duration': 9.388}, {'end': 258.998, 'text': 'and then how you can gain insights from that data.', 'start': 256.736, 'duration': 2.262}, {'end': 267.164, 'text': 'So now if you look at the image here earlier we had only structured data, which is in the organized form like DBMS or RDBMS or Oracle.', 'start': 259.658, 'duration': 7.506}, {'end': 271.409, 'text': 'But today we have both structured as well as unstructured data, which is unorganized.', 'start': 267.605, 'duration': 3.804}], 'summary': 'Data science enables analysis of structured and unstructured data for gaining insights.', 'duration': 24.061, 'max_score': 247.348, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY247348.jpg'}, {'end': 415.272, 'src': 'embed', 'start': 391.897, 'weight': 4, 'content': [{'end': 398.601, 'text': 'So first of all data scientist is responsible for designing and creating processes for your complex as well as a large scale data set.', 'start': 391.897, 'duration': 6.704}, {'end': 404.325, 'text': 'So these data sets are basically used for your modeling your data mining and for your research purposes as well.', 'start': 398.961, 'duration': 5.364}, {'end': 413.11, 'text': 'So a data scientist is involved in processing your data, cleaning your data, verifying all the integrities of data for your analysis purpose,', 'start': 404.925, 'duration': 8.185}, {'end': 415.272, 'text': 'and all these things are done by data scientist.', 'start': 413.11, 'duration': 2.162}], 'summary': 'A data scientist designs processes for complex and large-scale datasets used in modeling, data mining, and research.', 'duration': 23.375, 'max_score': 391.897, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY391897.jpg'}, {'end': 457.826, 'src': 'embed', 'start': 429.526, 'weight': 5, 'content': [{'end': 436.127, 'text': 'These operations are Discovery data preparation model planning model building operationalize and then communicating the results.', 'start': 429.526, 'duration': 6.601}, {'end': 439.028, 'text': 'So let us discuss all these operations one by one.', 'start': 436.707, 'duration': 2.321}, {'end': 442.749, 'text': 'So the first two steps are data Discovery and data preparation.', 'start': 439.748, 'duration': 3.001}, {'end': 447.782, 'text': 'So in discovery what you need to do you first need to understand all the specifications of your data.', 'start': 443.26, 'duration': 4.522}, {'end': 452.224, 'text': 'You should also know the requirements you should know the priorities and the required budget for the same.', 'start': 448.322, 'duration': 3.902}, {'end': 457.826, 'text': 'You should understand each and every aspect of data, where you know data is, in which form,', 'start': 452.604, 'duration': 5.222}], 'summary': 'Operations include discovery, preparation, understanding specifications, requirements, and priorities for budget.', 'duration': 28.3, 'max_score': 429.526, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY429526.jpg'}, {'end': 586.563, 'src': 'embed', 'start': 560.403, 'weight': 6, 'content': [{'end': 564.706, 'text': 'But here Python is the most preferred choice for data scientist among all of these.', 'start': 560.403, 'duration': 4.303}, {'end': 567.849, 'text': 'So let us understand why Python for data science.', 'start': 565.487, 'duration': 2.362}, {'end': 569.85, 'text': 'So Python has numerous features.', 'start': 568.469, 'duration': 1.381}, {'end': 571.171, 'text': 'So let me discuss one by one.', 'start': 569.87, 'duration': 1.301}, {'end': 574.079, 'text': 'So first of all, it is simple and it is easy to learn.', 'start': 571.738, 'duration': 2.341}, {'end': 577.84, 'text': "So it's a very powerful language and closely resembles your English language.", 'start': 574.399, 'duration': 3.441}, {'end': 584.182, 'text': "Now furthermore in Python, you don't have to deal with complex syntax like you used to do in Java or any other programming language.", 'start': 578.14, 'duration': 6.042}, {'end': 586.563, 'text': 'Next it is fit for many platforms.', 'start': 584.902, 'duration': 1.661}], 'summary': 'Python is the most preferred language for data science due to its simplicity, power, and platform adaptability.', 'duration': 26.16, 'max_score': 560.403, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY560403.jpg'}], 'start': 7.008, 'title': 'Python for data science and evolution', 'summary': "Covers a python for data science full course, emphasizing python's significance in data science, and discusses the evolution of data science, from handling vast amounts of structured and unstructured data to the role of data scientists, with an emphasis on python's importance.", 'chapters': [{'end': 230.901, 'start': 7.008, 'title': 'Python for data science course overview', 'summary': "Introduces a python for data science full course, covering the basics of data science, environment setup, statistics, python libraries, machine learning, deep learning, and pyspark, while emphasizing python's significance in data science and the need for data science due to the increasing unstructured data.", 'duration': 223.893, 'highlights': ["Python is the most preferred programming language for data science, making data science a lot easier with its out-of-the-box features. Python's out-of-the-box features have made it the top choice for data science, simplifying the process and contributing to its popularity.", 'The chapter covers six modules, including environment setup, statistics, Python libraries, machine learning, deep learning, and PySpark, providing a comprehensive overview of data science using Python. The course is divided into six modules, offering a comprehensive understanding of data science using Python, covering topics such as environment setup, statistics, Python libraries, machine learning, deep learning, and PySpark.', "The need for data science arises from the increasing unstructured or semi-structured data being generated, making traditional BI tools insufficient for analysis. The increasing unstructured or semi-structured data generation necessitates the need for data science, as traditional BI tools are insufficient for analysis in today's data landscape."]}, {'end': 616.875, 'start': 231.302, 'title': 'Evolution of data science', 'summary': 'Discusses the evolution of data science, from the challenges of handling vast amounts of structured and unstructured data to the role of data scientists in the data life cycle, and the importance of python as a preferred language for data science.', 'duration': 385.573, 'highlights': ['Data Science enables the handling of both structured and unstructured data, allowing for exploration and visualization, leading to scientific discoveries and storytelling. Data science has enabled the handling of both structured and unstructured data, allowing for exploration, visualization, scientific discoveries, and storytelling, leading to more effective insights and decision-making.', 'The role of a data scientist involves designing and creating processes for complex and large-scale datasets, data processing, cleaning, and building predictive models using machine learning algorithms. The role of a data scientist involves designing and creating processes for complex and large-scale datasets, data processing, cleaning, and building predictive models using machine learning algorithms, contributing to effective data analysis and decision-making.', 'The data life cycle involves operations such as data discovery, preparation, model planning, building, operationalization, and communicating the results, providing a comprehensive approach to handling data science projects. The data life cycle involves operations such as data discovery, preparation, model planning, building, operationalization, and communicating the results, providing a comprehensive approach to handling data science projects and delivering valuable insights.', 'Python is the most preferred choice for data scientists due to its simplicity, ease of learning, cross-platform support, high-level nature, and interpretability. Python is the most preferred choice for data scientists due to its simplicity, ease of learning, cross-platform support, high-level nature, and interpretability, making it a versatile and powerful language for data science.']}], 'duration': 609.867, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY7008.jpg', 'highlights': ["Python's out-of-the-box features have made it the top choice for data science, simplifying the process and contributing to its popularity.", 'The course is divided into six modules, offering a comprehensive understanding of data science using Python, covering topics such as environment setup, statistics, Python libraries, machine learning, deep learning, and PySpark.', "The increasing unstructured or semi-structured data generation necessitates the need for data science, as traditional BI tools are insufficient for analysis in today's data landscape.", 'Data science has enabled the handling of both structured and unstructured data, allowing for exploration, visualization, scientific discoveries, and storytelling, leading to more effective insights and decision-making.', 'The role of a data scientist involves designing and creating processes for complex and large-scale datasets, data processing, cleaning, and building predictive models using machine learning algorithms, contributing to effective data analysis and decision-making.', 'The data life cycle involves operations such as data discovery, preparation, model planning, building, operationalization, and communicating the results, providing a comprehensive approach to handling data science projects and delivering valuable insights.', 'Python is the most preferred choice for data scientists due to its simplicity, ease of learning, cross-platform support, high-level nature, and interpretability, making it a versatile and powerful language for data science.']}, {'end': 1620.33, 'segs': [{'end': 643.736, 'src': 'embed', 'start': 617.315, 'weight': 0, 'content': [{'end': 621.997, 'text': 'It is also less code intensive as compared to any traditional programming language.', 'start': 617.315, 'duration': 4.682}, {'end': 626.038, 'text': 'then you can perform data manipulation, analysis and visualization with Python.', 'start': 621.997, 'duration': 4.041}, {'end': 632.148, 'text': 'So for manipulation, we use libraries such as numpy and pandas will be discussing these libraries as well.', 'start': 626.604, 'duration': 5.544}, {'end': 638.312, 'text': 'and for visualization, we have matplotlib, seaborn and many more like these, but these are the most common ones that we use.', 'start': 632.148, 'duration': 6.164}, {'end': 643.736, 'text': 'and finally, Python comes with powerful libraries for machine learning applications and other scientific computations.', 'start': 638.312, 'duration': 5.424}], 'summary': 'Python enables data manipulation, analysis, and visualization with libraries like numpy, pandas, matplotlib, and seaborn; also supports machine learning and scientific computations.', 'duration': 26.421, 'max_score': 617.315, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY617315.jpg'}, {'end': 757.959, 'src': 'embed', 'start': 728.743, 'weight': 1, 'content': [{'end': 732.547, 'text': 'All of this requires tools which are much more sophisticated than Excel.', 'start': 728.743, 'duration': 3.804}, {'end': 741.462, 'text': 'So guys, data scientists need to be very efficient with coding languages, and few of the core languages associated with data science include SQL,', 'start': 733.177, 'duration': 8.285}, {'end': 743.023, 'text': 'Python R and SAS.', 'start': 741.462, 'duration': 1.561}, {'end': 747.786, 'text': 'It is also important for a data scientist to be a tactical business consultant.', 'start': 743.584, 'duration': 4.202}, {'end': 751.769, 'text': 'So guys, business problems can be only solved by data scientists.', 'start': 748.167, 'duration': 3.602}, {'end': 757.959, 'text': 'Since data scientists work so closely with data, they know everything about the business.', 'start': 752.296, 'duration': 5.663}], 'summary': 'Data scientists need proficiency in sql, python, r, and sas to solve business problems efficiently.', 'duration': 29.216, 'max_score': 728.743, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY728743.jpg'}, {'end': 837.099, 'src': 'embed', 'start': 810.939, 'weight': 3, 'content': [{'end': 816.624, 'text': 'Apart from that you should also have a good understanding of probability theory and descriptive statistics.', 'start': 810.939, 'duration': 5.685}, {'end': 820.007, 'text': 'These concepts will help you make better business decisions.', 'start': 817.104, 'duration': 2.903}, {'end': 828.015, 'text': "So no matter what type of company or role you're interviewing for, you're going to be expected to know how to use the tools of the trade.", 'start': 820.672, 'duration': 7.343}, {'end': 833.778, 'text': 'Okay, this means that you have to know a statistical programming language like R or Python.', 'start': 828.395, 'duration': 5.383}, {'end': 837.099, 'text': "And also you'll need to know a database querying language like SQL.", 'start': 833.798, 'duration': 3.301}], 'summary': 'Understanding probability theory and descriptive statistics is essential for making better business decisions, and knowing statistical programming languages like r or python as well as database querying language like sql is expected in various company roles.', 'duration': 26.16, 'max_score': 810.939, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY810939.jpg'}, {'end': 936.214, 'src': 'embed', 'start': 908.456, 'weight': 8, 'content': [{'end': 912.999, 'text': 'This is the most time consuming task because data wrangling is all about cleaning the data.', 'start': 908.456, 'duration': 4.543}, {'end': 919.303, 'text': 'There are a lot of instances where the data sets have missing values, or they have null values,', 'start': 913.379, 'duration': 5.924}, {'end': 922.305, 'text': 'or they have inconsistent formats or inconsistent values.', 'start': 919.303, 'duration': 3.002}, {'end': 925.167, 'text': 'Now you need to understand what to do with such values.', 'start': 922.685, 'duration': 2.482}, {'end': 928.449, 'text': 'This is where data wrangling or data cleaning comes into the picture.', 'start': 925.327, 'duration': 3.122}, {'end': 931.711, 'text': "Then after you're done with that, you are going to analyze the data.", 'start': 928.909, 'duration': 2.802}, {'end': 936.214, 'text': "So guys, after data wrangling and cleaning is done, you're going to start exploring.", 'start': 932.111, 'duration': 4.103}], 'summary': 'Data wrangling is time consuming due to cleaning inconsistent and missing values. after cleaning, data analysis begins.', 'duration': 27.758, 'max_score': 908.456, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY908456.jpg'}, {'end': 999.614, 'src': 'embed', 'start': 970.104, 'weight': 4, 'content': [{'end': 973.167, 'text': 'So there are a few algorithms like KNN or k-nearest neighbor.', 'start': 970.104, 'duration': 3.063}, {'end': 977.713, 'text': "There's random forest, there's k-means algorithm, there's support vector machines.", 'start': 973.508, 'duration': 4.205}, {'end': 981.657, 'text': 'All of these algorithms, you have to be aware of all of these algorithms.', 'start': 978.053, 'duration': 3.604}, {'end': 987.664, 'text': 'And let me tell you that most of these algorithms can be implemented using R or Python libraries.', 'start': 982.178, 'duration': 5.486}, {'end': 993.429, 'text': 'Okay, you need to have an understanding of machine learning if you have large amount of data in front of you,', 'start': 988.204, 'duration': 5.225}, {'end': 999.614, 'text': 'which is going to be the case for most of the people right now, because data is being generated at an unstoppable pace.', 'start': 993.429, 'duration': 6.185}], 'summary': 'Various machine learning algorithms like knn, random forest, k-means, and support vector machines can be implemented using r or python libraries, essential for handling large amounts of data being generated rapidly.', 'duration': 29.51, 'max_score': 970.104, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY970104.jpg'}, {'end': 1065.756, 'src': 'embed', 'start': 1023.775, 'weight': 5, 'content': [{'end': 1030.121, 'text': "So guys, we know that we've been generating a lot of data, and most of this data can be structured or unstructured as well.", 'start': 1023.775, 'duration': 6.346}, {'end': 1034.404, 'text': 'So on such data, you cannot use traditional data processing system.', 'start': 1030.621, 'duration': 3.783}, {'end': 1038.448, 'text': "So that's why you need to know frameworks like Hadoop and Spark.", 'start': 1034.865, 'duration': 3.583}, {'end': 1040.97, 'text': 'These frameworks can be used to handle big data.', 'start': 1038.488, 'duration': 2.482}, {'end': 1043.512, 'text': 'Lastly, we have data visualization.', 'start': 1041.431, 'duration': 2.081}, {'end': 1048.436, 'text': 'So guys, data visualization is one of the most important part of data analysis.', 'start': 1043.853, 'duration': 4.583}, {'end': 1054.804, 'text': 'It is always very important to present the data in an understandable and visually appealing format.', 'start': 1048.917, 'duration': 5.887}, {'end': 1059.429, 'text': 'So data visualization is one of the skills that data scientists have to master.', 'start': 1055.164, 'duration': 4.265}, {'end': 1065.756, 'text': 'If you want to communicate the data with the end users in a better way, then data visualization is a must.', 'start': 1060.03, 'duration': 5.726}], 'summary': 'Data scientists need to master hadoop, spark, and data visualization for handling and presenting structured and unstructured data effectively.', 'duration': 41.981, 'max_score': 1023.775, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY1023775.jpg'}, {'end': 1129.195, 'src': 'embed', 'start': 1103.433, 'weight': 2, 'content': [{'end': 1111.637, 'text': 'So, as data scientists have to understand the challenges of a business and they have to offer the best solution using data analysis and data processing.', 'start': 1103.433, 'duration': 8.204}, {'end': 1115.781, 'text': 'So, for instance, if they are expected to perform predictive analysis,', 'start': 1111.897, 'duration': 3.884}, {'end': 1121.487, 'text': 'they should also be able to identify trends and patterns that can help the companies in making better decisions.', 'start': 1115.781, 'duration': 5.706}, {'end': 1129.195, 'text': 'To become a data scientist, you have to be an expert in R, MATLAB, SQL, Python, and other complementary technologies.', 'start': 1121.867, 'duration': 7.328}], 'summary': 'Data scientists must offer solutions using data analysis, predictive analysis, and be experts in r, matlab, sql, python.', 'duration': 25.762, 'max_score': 1103.433, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY1103433.jpg'}, {'end': 1426.2, 'src': 'embed', 'start': 1384.833, 'weight': 7, 'content': [{'end': 1390.776, 'text': 'And like I said earlier, you need to have a good understanding of technologies like Python, SAS, R, Java, and etc.', 'start': 1384.833, 'duration': 5.943}, {'end': 1394.238, 'text': 'So guys, these were the different job roles in data science.', 'start': 1391.356, 'duration': 2.882}, {'end': 1395.918, 'text': 'I hope you all found this informative.', 'start': 1394.398, 'duration': 1.52}, {'end': 1399.32, 'text': "Now let's move ahead and look at the data life cycle.", 'start': 1396.259, 'duration': 3.061}, {'end': 1403.152, 'text': 'So guys, there are basically six steps in the data lifecycle.', 'start': 1400.131, 'duration': 3.021}, {'end': 1405.333, 'text': 'It starts with a business requirement.', 'start': 1403.572, 'duration': 1.761}, {'end': 1407.353, 'text': 'Next is the data acquisition.', 'start': 1405.673, 'duration': 1.68}, {'end': 1410.595, 'text': "After that, you'll process the data, which is called data processing.", 'start': 1407.774, 'duration': 2.821}, {'end': 1414.476, 'text': 'Then there is data exploration, modeling, and finally deployment.', 'start': 1410.935, 'duration': 3.541}, {'end': 1421.438, 'text': "So guys, before you even start on a data science project, it is important that you understand the problem you're trying to solve.", 'start': 1414.896, 'duration': 6.542}, {'end': 1426.2, 'text': "So in this stage, you're just going to focus on identifying the central objectives of the project.", 'start': 1421.678, 'duration': 4.522}], 'summary': 'Data science job roles and data lifecycle stages were discussed, including six steps: business requirement, data acquisition, data processing, data exploration, modeling, and deployment.', 'duration': 41.367, 'max_score': 1384.833, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY1384833.jpg'}, {'end': 1471.93, 'src': 'embed', 'start': 1443.11, 'weight': 9, 'content': [{'end': 1450.015, 'text': 'At this stage, some of the questions you can ask yourself is what data do I need for my project? Where does it live?', 'start': 1443.11, 'duration': 6.905}, {'end': 1455.039, 'text': 'How can I obtain it? And what is the most efficient way to store and access all of it?', 'start': 1450.115, 'duration': 4.924}, {'end': 1457.121, 'text': 'Next up, there is data processing.', 'start': 1455.54, 'duration': 1.581}, {'end': 1460.943, 'text': 'Now usually all the data that you collected is a huge mess.', 'start': 1457.661, 'duration': 3.282}, {'end': 1464.726, 'text': "Okay, it's not formatted, it's not structured, it's not cleaned.", 'start': 1461.163, 'duration': 3.563}, {'end': 1471.93, 'text': "So if you find any data set that is cleaned and it's packaged well for you, then you've actually won the lottery,", 'start': 1465.246, 'duration': 6.684}], 'summary': 'Identify needed data, process it efficiently, and find cleaned datasets for project success.', 'duration': 28.82, 'max_score': 1443.11, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY1443110.jpg'}], 'start': 617.315, 'title': 'Data science fundamentals', 'summary': 'Covers the advantages of using python for data manipulation, analysis, and visualization, emphasizes essential skills required for a data scientist, including proficiency in programming languages like r or python, database querying language like sql, data extraction and processing, data wrangling, machine learning, big data processing frameworks, data visualization, various job roles within data science, the different job roles in data science, the six steps of the data lifecycle, and the key processes involved.', 'chapters': [{'end': 828.015, 'start': 617.315, 'title': 'Python for data science & skills of a data scientist', 'summary': 'Covers the advantages of using python for data manipulation, analysis, and visualization, and highlights the skills required for a data scientist, including knowledge of mathematics, technology, and business acumen.', 'duration': 210.7, 'highlights': ['Python provides powerful libraries for data manipulation, analysis, and visualization, such as numpy, pandas, matplotlib, and seaborn. Python offers libraries like numpy and pandas for data manipulation, and matplotlib and seaborn for visualization, making it less code intensive and suitable for data science applications.', 'Data scientists require knowledge of mathematics, including statistics, linear algebra, and probability theory, for building predictive models and understanding algorithms. Data scientists need a strong understanding of mathematics, including statistics, linear algebra, and probability theory, to build predictive models and algorithms for data analysis.', 'Proficiency in technology and coding languages such as SQL, Python, R, and SAS are essential for data scientists to handle complex algorithms and enormous datasets. Data scientists need to be proficient in coding languages like SQL, Python, R, and SAS to handle complex algorithms and large datasets efficiently.', 'Business acumen is crucial for data scientists, as they need to understand the business context, analyze the data, and make strategic decisions based on the insights gained. Data scientists must have business acumen to understand the business context, analyze data, and make strategic decisions based on insights gained from the data.', 'Statistics, probability theory, and descriptive statistics are fundamental skill sets required for data scientists to make informed business decisions. A strong understanding of statistics, probability theory, and descriptive statistics is essential for data scientists to make informed business decisions.']}, {'end': 1384.373, 'start': 828.395, 'title': 'Essential skills for data scientists', 'summary': 'Emphasizes the essential skills required to become a data scientist, including proficiency in programming languages like r or python and database querying language like sql, data extraction and processing, data wrangling, machine learning, big data processing frameworks, data visualization, and various job roles within data science.', 'duration': 555.978, 'highlights': ['Proficiency in Programming Languages and Database Querying Data scientists need to be proficient in programming languages like R or Python and database querying language like SQL, as these languages offer a wide range of packages and predefined algorithms that facilitate data analysis and processing.', 'Data Wrangling and Cleaning Data wrangling, involving cleaning and organizing data, is a time-consuming task in data science, requiring the handling of missing values, null values, inconsistent formats, and values, before proceeding to data analysis.', 'Machine Learning Algorithms Proficiency in machine learning algorithms like KNN, random forest, k-means, and support vector machines is crucial for processing large amounts of data, as these algorithms can be implemented using R or Python libraries.', 'Big Data Processing Frameworks Understanding frameworks like Hadoop and Spark is essential for handling structured and unstructured big data, as traditional data processing systems are inadequate for processing such data.', 'Data Visualization Skills Data visualization is vital for presenting data in an understandable and visually appealing format, and mastering tools like Tableau and Power BI is crucial for effective communication of data to end users.']}, {'end': 1620.33, 'start': 1384.833, 'title': 'Data science job roles & lifecycle', 'summary': 'Covers the different job roles in data science, the six steps of the data lifecycle, and the key processes involved, emphasizing the importance of understanding the problem, data acquisition, processing, exploration, modeling, and deployment. it also highlights the technologies required and the challenges in data processing and modeling.', 'duration': 235.497, 'highlights': ['Data Lifecycle Steps The data lifecycle involves six steps: business requirement, data acquisition, processing, exploration, modeling, and deployment, emphasizing the importance of understanding the problem before starting a data science project.', 'Data Processing Challenges Data processing involves cleaning and structuring data, which can be time-consuming and effort-intensive due to the need to identify missing, inconsistent, and unnecessary data, requiring a significant amount of time and effort.', 'Data Modeling Process The data modeling process includes model training, involving splitting the input data into training and testing datasets, building and evaluating the model using machine learning algorithms to find the most suitable model for the business requirement.', 'Data Acquisition Importance The stage of data acquisition is crucial, involving the process of gathering data from different sources and addressing key questions such as what data is needed, where it is located, and the most efficient way to store and access it.', 'Data Science Job Roles The chapter highlights the need for a good understanding of technologies like Python, SAS, R, and Java for different job roles in data science, providing essential information for individuals interested in pursuing a career in this field.']}], 'duration': 1003.015, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY617315.jpg', 'highlights': ['Python offers libraries like numpy and pandas for data manipulation, and matplotlib and seaborn for visualization, making it less code intensive and suitable for data science applications.', 'Data scientists need to be proficient in coding languages like SQL, Python, R, and SAS to handle complex algorithms and large datasets efficiently.', 'Data scientists must have business acumen to understand the business context, analyze data, and make strategic decisions based on insights gained from the data.', 'A strong understanding of statistics, probability theory, and descriptive statistics is essential for data scientists to make informed business decisions.', 'Proficiency in machine learning algorithms like KNN, random forest, k-means, and support vector machines is crucial for processing large amounts of data, as these algorithms can be implemented using R or Python libraries.', 'Understanding frameworks like Hadoop and Spark is essential for handling structured and unstructured big data, as traditional data processing systems are inadequate for processing such data.', 'Data visualization is vital for presenting data in an understandable and visually appealing format, and mastering tools like Tableau and Power BI is crucial for effective communication of data to end users.', 'The data lifecycle involves six steps: business requirement, data acquisition, processing, exploration, modeling, and deployment, emphasizing the importance of understanding the problem before starting a data science project.', 'Data processing involves cleaning and structuring data, which can be time-consuming and effort-intensive due to the need to identify missing, inconsistent, and unnecessary data, requiring a significant amount of time and effort.', 'The stage of data acquisition is crucial, involving the process of gathering data from different sources and addressing key questions such as what data is needed, where it is located, and the most efficient way to store and access it.', 'The chapter highlights the need for a good understanding of technologies like Python, SAS, R, and Java for different job roles in data science, providing essential information for individuals interested in pursuing a career in this field.']}, {'end': 2905.336, 'segs': [{'end': 1673.298, 'src': 'embed', 'start': 1641.905, 'weight': 0, 'content': [{'end': 1643.626, 'text': 'other scientists use a lab notebook.', 'start': 1641.905, 'duration': 1.721}, {'end': 1648.061, 'text': 'Now the Jupyter product was originally developed as a part of IPython project.', 'start': 1644.139, 'duration': 3.922}, {'end': 1652.664, 'text': 'The IPython project was used to provide interactive online access to Python.', 'start': 1648.541, 'duration': 4.123}, {'end': 1658.507, 'text': 'Over time it became useful to interact with other data analysis tools such as R in the same manner.', 'start': 1653.124, 'duration': 5.383}, {'end': 1663.69, 'text': 'With the split from Python, the tool grew into its current manifestation of Jupyter.', 'start': 1658.987, 'duration': 4.703}, {'end': 1667.253, 'text': "Now, IPython is still an active tool that's available for use.", 'start': 1664.27, 'duration': 2.983}, {'end': 1673.298, 'text': 'The name Jupyter itself is derived from the combination of Julia, Python, and R.', 'start': 1667.853, 'duration': 5.445}], 'summary': 'Jupyter, originally part of ipython, now supports julia, python, and r.', 'duration': 31.393, 'max_score': 1641.905, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY1641905.jpg'}, {'end': 1722.599, 'src': 'embed', 'start': 1684.975, 'weight': 1, 'content': [{'end': 1691.257, 'text': 'it is strongly recommended installing Python and Jupyter using Anaconda distribution, which includes Python,', 'start': 1684.975, 'duration': 6.282}, {'end': 1696.419, 'text': 'the Jupyter Notebook and other commonly used packages for scientific computing as well as data science.', 'start': 1691.257, 'duration': 5.162}, {'end': 1700.1, 'text': 'Although one can also do so using the pip installation method.', 'start': 1696.999, 'duration': 3.101}, {'end': 1708.463, 'text': 'Personally, what I would suggest is downloading Anaconda Navigator, which is a desktop graphical user interface included in Anaconda.', 'start': 1700.64, 'duration': 7.823}, {'end': 1716.835, 'text': 'Now this allows you to launch application and easily manage conda packages environments and channels without the need to use command line commands.', 'start': 1709.071, 'duration': 7.764}, {'end': 1722.599, 'text': 'So all you need to do is go to anaconda.org and inside you go to anaconda navigator.', 'start': 1717.276, 'duration': 5.323}], 'summary': 'Anaconda distribution is recommended for python and jupyter installation, including commonly used packages for scientific computing and data science.', 'duration': 37.624, 'max_score': 1684.975, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY1684975.jpg'}, {'end': 1799.847, 'src': 'embed', 'start': 1763.862, 'weight': 3, 'content': [{'end': 1769.229, 'text': 'Now by definition a Jupyter notebook is fundamentally a JSON file with a number of annotations.', 'start': 1763.862, 'duration': 5.367}, {'end': 1774.297, 'text': 'Now it has three main parts which are the metadata, the notebook format and the list of cells.', 'start': 1769.73, 'duration': 4.567}, {'end': 1781.022, 'text': 'Now, you should get yourself acquainted with the environment that Jupyter user interface has a number of components.', 'start': 1775.2, 'duration': 5.822}, {'end': 1787.523, 'text': "So it's important to know what are components you should be using on a daily basis and you should get acquainted with it.", 'start': 1781.462, 'duration': 6.061}, {'end': 1791.324, 'text': 'So as you can see here our focus today will be on the Jupyter notebook.', 'start': 1788.004, 'duration': 3.32}, {'end': 1793.205, 'text': 'So let me just launch the Jupyter notebook.', 'start': 1791.345, 'duration': 1.86}, {'end': 1798.206, 'text': 'Now what it does is creates a online python instance for you to use it over the web.', 'start': 1793.645, 'duration': 4.561}, {'end': 1799.847, 'text': 'So let it launch.', 'start': 1798.587, 'duration': 1.26}], 'summary': 'Jupyter notebook is a json file with metadata, format, and cells. it provides an online python instance for web use.', 'duration': 35.985, 'max_score': 1763.862, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY1763862.jpg'}, {'end': 1842.647, 'src': 'embed', 'start': 1817.419, 'weight': 4, 'content': [{'end': 1824.143, 'text': "and what we'll do is we'll understand all of these three and understand what are the importance of these three tabs.", 'start': 1817.419, 'duration': 6.724}, {'end': 1828.746, 'text': 'or the file tab shows the list of the current files in the directory.', 'start': 1824.143, 'duration': 4.603}, {'end': 1831.068, 'text': 'so as you can see, we have so many files here.', 'start': 1828.746, 'duration': 2.322}, {'end': 1837.903, 'text': 'now the running tab presents another screen of the currently running processes and the notebooks.', 'start': 1831.838, 'duration': 6.065}, {'end': 1842.647, 'text': 'now the drop-down list for the terminals and notebooks are populated with their running numbers.', 'start': 1837.903, 'duration': 4.744}], 'summary': 'Understanding the importance of file, running, and drop-down tabs in jupyter, with many files and running processes.', 'duration': 25.228, 'max_score': 1817.419, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY1817419.jpg'}, {'end': 1891.713, 'src': 'embed', 'start': 1853.475, 'weight': 5, 'content': [{'end': 1859.34, 'text': 'so in the top right corner of the screen there are three buttons which are upload, new and the refresh button.', 'start': 1853.475, 'duration': 5.865}, {'end': 1860.181, 'text': 'let me go back here.', 'start': 1859.34, 'duration': 0.841}, {'end': 1863.453, 'text': 'So, as you can see, here we have the upload new and the refresh button.', 'start': 1860.731, 'duration': 2.722}, {'end': 1871.097, 'text': 'Now the upload button is used to add files to the notebook space and you may also just drag and drop as you would when handling files.', 'start': 1864.013, 'duration': 7.084}, {'end': 1875.139, 'text': 'Similarly you can drag and drop notebooks into specific folders as well.', 'start': 1871.657, 'duration': 3.482}, {'end': 1883.351, 'text': 'Now the menu with the new in the top presents a further menu of text file folders terminal and Python 3.', 'start': 1875.66, 'duration': 7.691}, {'end': 1886.732, 'text': 'Now the text file option is used to add a text file to the current directory.', 'start': 1883.351, 'duration': 3.381}, {'end': 1891.713, 'text': 'Now Jupyter will open a new browser window for you for the running new text editor.', 'start': 1887.132, 'duration': 4.581}], 'summary': "The interface has three buttons: upload, new, and refresh. it allows file and notebook upload via drag and drop. the 'new' menu offers options for text files, folders, terminals, and python 3.", 'duration': 38.238, 'max_score': 1853.475, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY1853475.jpg'}, {'end': 2131.539, 'src': 'embed', 'start': 2089.824, 'weight': 7, 'content': [{'end': 2093.828, 'text': 'This can be a problem if malicious aspects have been placed in the notebook.', 'start': 2089.824, 'duration': 4.004}, {'end': 2102.215, 'text': 'Now the default security mechanism for Jupyter Notebooks include raw HTML, which is always sanitized and checked for malicious coding.', 'start': 2094.391, 'duration': 7.824}, {'end': 2109.878, 'text': "Another aspect is you cannot run external JavaScript's another cell contents, especially the HTML and the JavaScript's are not trusted.", 'start': 2102.775, 'duration': 7.103}, {'end': 2114.98, 'text': 'It requires user validation to continue and the output from any cell is not trusted.', 'start': 2110.218, 'duration': 4.762}, {'end': 2122.371, 'text': 'All other HTML or JavaScript is never trusted, and clearing the output will cause the notebook to become trusted when safe.', 'start': 2115.544, 'duration': 6.827}, {'end': 2128.236, 'text': 'now, notebooks can also use a security digest to ensure the correct user is modifying the content.', 'start': 2122.371, 'duration': 5.865}, {'end': 2131.539, 'text': 'So for that, what you need to do is a digest.', 'start': 2128.677, 'duration': 2.862}], 'summary': 'Jupyter notebooks default security includes sanitized html, user validation, and a security digest for ensuring content is trusted and modified by the correct user.', 'duration': 41.715, 'max_score': 2089.824, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY2089824.jpg'}, {'end': 2212.514, 'src': 'embed', 'start': 2187.846, 'weight': 13, 'content': [{'end': 2194.693, 'text': 'So code mirror, what it basically is, it is a JavaScript based editor for the use within the web pages and notebooks.', 'start': 2187.846, 'duration': 6.847}, {'end': 2197.155, 'text': 'So what you do is what you do code mirror.', 'start': 2194.693, 'duration': 2.462}, {'end': 2202.766, 'text': 'So, as you can see here, code mirror is a versatile text editor implemented in JavaScript for the browser.', 'start': 2197.842, 'duration': 4.924}, {'end': 2207.25, 'text': 'So what it does is allow you to configure the options for Jupiter.', 'start': 2203.186, 'duration': 4.064}, {'end': 2212.514, 'text': "So now let's execute some python code and understand the notebook in a better way.", 'start': 2207.91, 'duration': 4.604}], 'summary': 'Code mirror is a versatile javascript text editor for web pages and notebooks, allowing configuration for jupyter.', 'duration': 24.668, 'max_score': 2187.846, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY2187846.jpg'}, {'end': 2280.602, 'src': 'embed', 'start': 2252.137, 'weight': 10, 'content': [{'end': 2255.842, 'text': 'so So, as you can see, we have renamed this particular cell.', 'start': 2252.137, 'duration': 3.705}, {'end': 2263.47, 'text': 'Now auto save option should be on the next to the title as you can see last checkpoint a few days ago unsaved changes.', 'start': 2256.463, 'duration': 7.007}, {'end': 2265.912, 'text': 'The auto save option is always on.', 'start': 2264.21, 'duration': 1.702}, {'end': 2274.177, 'text': 'What we do is with an accurate name, we can find the selection and this particular notebook very easily from the notebook home page.', 'start': 2266.63, 'duration': 7.547}, {'end': 2280.602, 'text': "So if you select your browser's home tab and refresh, you will find this new window name displayed here again.", 'start': 2274.657, 'duration': 5.945}], 'summary': 'Renamed cell for easy access, auto save option always on.', 'duration': 28.465, 'max_score': 2252.137, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY2252137.jpg'}, {'end': 2436.595, 'src': 'embed', 'start': 2407.658, 'weight': 11, 'content': [{'end': 2414.082, 'text': "Now, it's interesting that Jupyter keeps track of the output last generated in the saved version of the file and is a save checkpoints.", 'start': 2407.658, 'duration': 6.424}, {'end': 2420.546, 'text': 'Now, if you were to rerun your cells using the rerun or the run all, the output would be generated and saved via autosave.', 'start': 2414.562, 'duration': 5.984}, {'end': 2422.687, 'text': 'Now, the cell number is incremented.', 'start': 2421.006, 'duration': 1.681}, {'end': 2427.69, 'text': 'And as you can see, if I rerun this, you see the cell number change from one to three.', 'start': 2423.107, 'duration': 4.583}, {'end': 2430.692, 'text': 'And if I rerun this, the cell number will change from two to four.', 'start': 2428.11, 'duration': 2.582}, {'end': 2436.595, 'text': 'so what Jupyter does is keeps a track of the latest version of each cell.', 'start': 2431.272, 'duration': 5.323}], 'summary': 'Jupyter saves and tracks output, increments cell numbers upon rerun.', 'duration': 28.937, 'max_score': 2407.658, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY2407658.jpg'}, {'end': 2492.594, 'src': 'embed', 'start': 2467.683, 'weight': 12, 'content': [{'end': 2473.086, 'text': 'then how does Python access a last data set or data set work in Jupiter?', 'start': 2467.683, 'duration': 5.403}, {'end': 2477.368, 'text': 'So let me create another new python notebook.', 'start': 2473.706, 'duration': 3.662}, {'end': 2481.21, 'text': "So what I'm going to do is name this as pandas.", 'start': 2478.188, 'duration': 3.022}, {'end': 2487.715, 'text': "So from here, what we'll do is read in large data set and compute some standard statistics of data.", 'start': 2482.19, 'duration': 5.525}, {'end': 2492.594, 'text': 'Now what we are interested in and seeing how to use the pandas in Jupyter,', 'start': 2488.331, 'duration': 4.263}], 'summary': 'Python accesses and computes statistics on a large data set using pandas in jupyter.', 'duration': 24.911, 'max_score': 2467.683, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY2467683.jpg'}, {'end': 2662.66, 'src': 'embed', 'start': 2637.627, 'weight': 15, 'content': [{'end': 2645.815, 'text': 'Parnas is one of the most widely used features of Python and it is a third-party library for data analysis packages and that can be used freely.', 'start': 2637.627, 'duration': 8.188}, {'end': 2652.872, 'text': 'So in this example, we will develop a Python script that uses Pandas to see if there are any effect to using it in the Jupyter.', 'start': 2646.647, 'duration': 6.225}, {'end': 2658.617, 'text': 'Now the result will be calculated using the survival rates of the Titanic passenger based on this set.', 'start': 2653.312, 'duration': 5.305}, {'end': 2662.66, 'text': "So we're going to use the Titanic data sets here, which is one of the most famous data set.", 'start': 2658.657, 'duration': 4.003}], 'summary': 'Using pandas in python for analyzing titanic survival rates.', 'duration': 25.033, 'max_score': 2637.627, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY2637627.jpg'}, {'end': 2752.179, 'src': 'embed', 'start': 2718.782, 'weight': 14, 'content': [{'end': 2720.444, 'text': 'And let me just rerun this.', 'start': 2718.782, 'duration': 1.662}, {'end': 2723.066, 'text': 'So as you can see here, we have the train set head.', 'start': 2721.164, 'duration': 1.902}, {'end': 2726.929, 'text': 'See the age, bag, and the ticket.', 'start': 2723.906, 'duration': 3.023}, {'end': 2729.41, 'text': "Then I'll split the data set again.", 'start': 2727.669, 'duration': 1.741}, {'end': 2732.333, 'text': 'Let me calculate the survival rate.', 'start': 2730.431, 'duration': 1.902}, {'end': 2736.476, 'text': "So if you have a look at the women's survival rate, that is 74%.", 'start': 2732.353, 'duration': 4.123}, {'end': 2741.354, 'text': "And if you have a look at the men's survival rate, that is 18%.", 'start': 2736.476, 'duration': 4.878}, {'end': 2743.955, 'text': 'So this was how we use the pandas library.', 'start': 2741.354, 'duration': 2.601}, {'end': 2752.179, 'text': 'But how does python graphics works in Jupiter? Let me just rename this one to python graphics or let me just name it to graphics.', 'start': 2743.995, 'duration': 8.184}], 'summary': 'Data analysis revealed a 74% survival rate for women and 18% for men using the pandas library in python.', 'duration': 33.397, 'max_score': 2718.782, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY2718782.jpg'}], 'start': 1620.33, 'title': 'Setting up and using jupyter notebook', 'summary': 'Covers setting up jupyter notebook with python and anaconda, exploring interface functionalities, security measures, understanding jupyter and python in notebooks, and pandas data analysis for the titanic dataset.', 'chapters': [{'end': 1799.847, 'start': 1620.33, 'title': 'Setting up jupyter notebook', 'summary': 'Explains how to set up jupyter notebook and its incorporation of statistics and math in data science, emphasizing the importance of python and the anaconda distribution for installation.', 'duration': 179.517, 'highlights': ['Jupyter notebook is a modern-day tool that allows data scientists to record their complete analysis process, similar to how scientists used lab notebooks in the early days. ', 'The Jupyter product was originally developed as a part of IPython project, providing interactive online access to Python and other data analysis tools such as R. ', 'Python is a requirement for installing the Jupyter notebook itself, and it is strongly recommended to install Python and Jupyter using the Anaconda distribution. ', 'Downloading Anaconda Navigator is suggested, as it provides a desktop graphical user interface for easily managing conda packages, environments, and channels without using command line commands. ', 'A Jupyter notebook is fundamentally a JSON file with metadata, notebook format, and a list of cells, and it is important to get acquainted with the Jupyter user interface components. ']}, {'end': 2051.944, 'start': 1800.607, 'title': 'Jupyter notebook interface', 'summary': 'Explains the layout and functionalities of a jupyter notebook interface, including the tabs for files, running processes, and clusters, as well as the options for uploading files, creating new files and folders, and using the refresh, checkbox, drop-down menu, and home button functionalities.', 'duration': 251.337, 'highlights': ['The file tab displays the list of current files in the directory, showing many files available. The file tab in the Jupyter Notebook interface provides a list of the current files in the directory, with a large number of files available.', 'The drop-down menu presents a list of choices available, such as folders, all notebooks, running files, and updates the counts accordingly, such as displaying 18 folders and 7 files. The drop-down menu allows users to select various options, including folders, all notebooks, running files, and updates the counts accordingly, displaying 18 folders and 7 files.', 'The upload button is used to add files to the notebook space, and users can also drag and drop files into specific folders. The upload button facilitates adding files to the notebook space, and users can also utilize drag and drop functionality to organize files into specific folders.']}, {'end': 2168.992, 'start': 2052.518, 'title': 'Security measures in jupyter notebooks', 'summary': 'Discusses the typical workflow of a jupyter notebook, the security concerns associated with sharing notebooks over the internet, and the measures to ensure security, including the use of security digest and notebook secret.', 'duration': 116.474, 'highlights': ['Jupyter notebooks are created to be shared with other users, but they can execute arbitrary code and generate arbitrary output, posing a security risk.', 'The default security mechanisms for Jupyter Notebooks include sanitizing raw HTML and prohibiting the running of external JavaScript and untrusted HTML or JavaScript.', 'Security digest is used to ensure the correct user is modifying the content by combining the entire notebook contents and a secret known only by the notebook creator, preventing the addition of malicious code.', "Users can add security to notebooks using the 'jupyter notebook --generate-config' command, replacing the notebook secret with a unique key to share with authorized users."]}, {'end': 2636.606, 'start': 2169.472, 'title': 'Understanding jupyter and python in notebooks', 'summary': 'Introduces jupyter features and functionalities, including configuration options, code execution, file management, auto-saving, and python data access in jupyter, emphasizing the use of code mirror for notebook display and modification, and showcasing the execution of python code in notebooks with examples of auto-saving, file management, and data access.', 'duration': 467.134, 'highlights': ["Jupyter does not interact with your scripts as much as it executes your script and records the result. Explanation of Jupyter's script execution and result recording process.", 'Auto save option is always on, assisting in finding the selection and the notebook easily from the notebook home page. Emphasis on the auto-save feature for easy access and management of notebooks.', "The cell number is incremented when rerunning cells, and Jupyter keeps track of the latest version of each cell. Description of Jupyter's cell management and version tracking.", "Demonstration of accessing and processing a data set using Python's pandas library within Jupyter, including importing the iris data set, basic statistics calculation, and displaying results. Overview of accessing and processing a data set using pandas in Jupyter, showcasing the import, basic statistics calculation, and result display.", 'Introduction to code mirror as a JavaScript-based editor for web pages and notebooks, allowing configuration options for Jupyter. Introduction and purpose of code mirror as a JavaScript-based editor for Jupyter with configuration options.']}, {'end': 2905.336, 'start': 2637.627, 'title': 'Pandas data analysis in jupyter', 'summary': 'Explores the usage of pandas library in jupyter to analyze the titanic dataset, demonstrating the calculation of survival rates for men and women, and also showcases the integration of python graphics for data visualization.', 'duration': 267.709, 'highlights': ['The survival rates of the Titanic passenger based on this set were calculated, revealing a 74% survival rate for women and an 18% survival rate for men. The chapter demonstrates the calculation of survival rates for men and women using the Titanic dataset, with women exhibiting a 74% survival rate and men showing an 18% survival rate.', 'The usage of Pandas library in Jupyter for data analysis and visualization was showcased, along with the integration of Python graphics for graphical display of data. The chapter showcases the usage of Pandas library in Jupyter for data analysis and visualization, as well as the integration of Python graphics for graphical display of data.', 'A demonstration of creating a new Python script in Jupyter and exploring various elements and shortcuts of Jupyter Notebook was provided. The chapter provides a demonstration of creating a new Python script in Jupyter and explores various elements and shortcuts of Jupyter Notebook.']}], 'duration': 1285.006, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY1620330.jpg', 'highlights': ['Jupyter notebook is a modern-day tool for data scientists, similar to lab notebooks.', 'Python is a requirement for installing Jupyter, recommended to use Anaconda distribution.', 'Downloading Anaconda Navigator is suggested for easily managing conda packages.', 'Jupyter notebook is fundamentally a JSON file with metadata and a list of cells.', 'The file tab in Jupyter displays the list of current files in the directory.', 'The drop-down menu in Jupyter presents a list of choices available.', 'The upload button in Jupyter facilitates adding files to the notebook space.', 'Jupyter notebooks can execute arbitrary code and generate arbitrary output, posing a security risk.', 'Default security mechanisms for Jupyter include sanitizing raw HTML and prohibiting external JavaScript.', 'Security digest is used to ensure the correct user is modifying the content.', 'Auto save option in Jupyter assists in finding the notebook easily from the notebook home page.', 'Jupyter keeps track of the latest version of each cell and increments the cell number when rerunning cells.', "Demonstration of accessing and processing a data set using Python's pandas library within Jupyter.", 'Introduction to code mirror as a JavaScript-based editor for web pages and notebooks.', 'The chapter demonstrates the calculation of survival rates for men and women using the Titanic dataset.', 'The chapter showcases the usage of Pandas library in Jupyter for data analysis and visualization.', 'The chapter provides a demonstration of creating a new Python script in Jupyter and explores various elements and shortcuts of Jupyter Notebook.']}, {'end': 4411.687, 'segs': [{'end': 3020.679, 'src': 'embed', 'start': 2955.955, 'weight': 0, 'content': [{'end': 2963.503, 'text': "Then we'll discuss what exactly statistics is, the basic terminologies in statistics, and a couple of sampling techniques.", 'start': 2955.955, 'duration': 7.548}, {'end': 2970.21, 'text': "Once we're done with that, we'll discuss the different types of statistics which involve descriptive and inferential statistics.", 'start': 2964.188, 'duration': 6.022}, {'end': 2974.471, 'text': "Then in the next session, we'll mainly be focusing on descriptive statistics.", 'start': 2970.71, 'duration': 3.761}, {'end': 2980.913, 'text': "Here we'll understand the different measures of center, measures of spread, information gain, and entropy.", 'start': 2975.012, 'duration': 5.901}, {'end': 2984.895, 'text': "We'll also understand all of these measures with the help of a use case.", 'start': 2981.354, 'duration': 3.541}, {'end': 2988.716, 'text': "And finally, we'll discuss what exactly a confusion matrix is.", 'start': 2985.395, 'duration': 3.321}, {'end': 2995.079, 'text': "Once we've covered the entire descriptive statistics module, we'll discuss the probability module.", 'start': 2989.515, 'duration': 5.564}, {'end': 3001.062, 'text': "Here we'll understand what exactly probability is, the different terminologies in probability.", 'start': 2995.499, 'duration': 5.563}, {'end': 3004.784, 'text': "We'll also study the different probability distributions.", 'start': 3001.723, 'duration': 3.061}, {'end': 3012.269, 'text': "Then we'll discuss the types of probability which include marginal probability, joint, and conditional probability.", 'start': 3005.325, 'duration': 6.944}, {'end': 3020.679, 'text': "Then we'll move on and discuss a use case wherein we'll see examples that show us how the different types of probability work.", 'start': 3013.647, 'duration': 7.032}], 'summary': 'Introduction to statistics, descriptive statistics, probability, and probability distributions with use cases.', 'duration': 64.724, 'max_score': 2955.955, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY2955955.jpg'}, {'end': 3094.277, 'src': 'embed', 'start': 3064.628, 'weight': 3, 'content': [{'end': 3070.752, 'text': "We'll also discuss margin of error and we'll understand all of these concepts by looking at a small use case.", 'start': 3064.628, 'duration': 6.124}, {'end': 3077.213, 'text': "We'll finally end the inferential statistic module by looking at what hypothesis testing is.", 'start': 3071.352, 'duration': 5.861}, {'end': 3081.694, 'text': 'Hypothesis testing is a very important part of inferential statistics,', 'start': 3077.673, 'duration': 4.021}, {'end': 3087.195, 'text': "so we'll end the session by looking at a use case that discusses how hypothesis testing works.", 'start': 3081.694, 'duration': 5.501}, {'end': 3094.277, 'text': "All right, so guys, there's a lot to cover today, so let's move ahead and take a look at our first topic, which is what is data.", 'start': 3087.596, 'duration': 6.681}], 'summary': 'Understanding margin of error, hypothesis testing, and data in inferential statistics.', 'duration': 29.649, 'max_score': 3064.628, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY3064628.jpg'}, {'end': 3180.465, 'src': 'embed', 'start': 3156.596, 'weight': 4, 'content': [{'end': 3163.944, 'text': 'Under qualitative data we have nominal and ordinal data and under quantitative data we have discrete and continuous data.', 'start': 3156.596, 'duration': 7.348}, {'end': 3167.035, 'text': "Now let's focus on qualitative data.", 'start': 3164.753, 'duration': 2.282}, {'end': 3175.441, 'text': "Now this type of data deals with characteristics and descriptors that can't be easily measured but can be observed subjectively.", 'start': 3167.055, 'duration': 8.386}, {'end': 3180.465, 'text': 'Now qualitative data is further divided into nominal and ordinal data.', 'start': 3176.042, 'duration': 4.423}], 'summary': 'Qualitative data: nominal, ordinal. quantitative data: discrete, continuous.', 'duration': 23.869, 'max_score': 3156.596, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY3156596.jpg'}, {'end': 3453.892, 'src': 'embed', 'start': 3430.157, 'weight': 5, 'content': [{'end': 3440.581, 'text': 'Now coming to the formal definition of statistics, statistics is an area of applied mathematics which is concerned with data collection, analysis,', 'start': 3430.157, 'duration': 10.424}, {'end': 3442.422, 'text': 'interpretation and presentation.', 'start': 3440.581, 'duration': 1.841}, {'end': 3448.004, 'text': 'Now usually when I speak about statistics, people think statistics is all about analysis.', 'start': 3442.802, 'duration': 5.202}, {'end': 3451.331, 'text': 'but statistics has other parts to it.', 'start': 3448.849, 'duration': 2.482}, {'end': 3453.892, 'text': 'It has data collection is also a part of statistics.', 'start': 3451.411, 'duration': 2.481}], 'summary': 'Statistics is an area of applied mathematics concerned with data collection, analysis, interpretation, and presentation.', 'duration': 23.735, 'max_score': 3430.157, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY3430157.jpg'}, {'end': 3512.726, 'src': 'embed', 'start': 3479.088, 'weight': 9, 'content': [{'end': 3484.373, 'text': "okay, let's say that your company has created a new drug that may cure cancer.", 'start': 3479.088, 'duration': 5.285}, {'end': 3488.997, 'text': "how would you conduct a test to confirm the drug's effectiveness?", 'start': 3484.373, 'duration': 4.624}, {'end': 3493.802, 'text': 'now, even though this sounds like a biology problem, this can be solved with statistics.', 'start': 3488.997, 'duration': 4.805}, {'end': 3498.126, 'text': 'all right, you will have to create a test which can confirm the effectiveness of the drug.', 'start': 3493.802, 'duration': 4.324}, {'end': 3502.189, 'text': 'all right, this is a common problem that can be solved using statistics.', 'start': 3498.126, 'duration': 4.063}, {'end': 3503.591, 'text': 'let me give you another example.', 'start': 3502.189, 'duration': 1.402}, {'end': 3512.726, 'text': 'You and a friend are at a baseball game and, out of the blue, he offers you a bet that neither team will hit a home run in that game.', 'start': 3504.361, 'duration': 8.365}], 'summary': "Using statistics, design a test to confirm a new drug's effectiveness.", 'duration': 33.638, 'max_score': 3479.088, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY3479088.jpg'}, {'end': 3580.798, 'src': 'embed', 'start': 3554.898, 'weight': 6, 'content': [{'end': 3565.647, 'text': 'and the basic idea behind data analysis is to use statistical techniques in order to figure out the relationship between different variables or different components in your business.', 'start': 3554.898, 'duration': 10.749}, {'end': 3569.39, 'text': "okay?. So now let's move on and look at our next topic.", 'start': 3565.647, 'duration': 3.743}, {'end': 3572.432, 'text': 'which is basic terminologies in statistics.', 'start': 3570.171, 'duration': 2.261}, {'end': 3580.798, 'text': 'Now before you dive deep into statistics, it is important that you understand basic terminologies used in statistics.', 'start': 3573.713, 'duration': 7.085}], 'summary': 'Data analysis uses statistical techniques to understand relationships between variables. next topic: basic terminologies in statistics.', 'duration': 25.9, 'max_score': 3554.898, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY3554898.jpg'}, {'end': 3622.828, 'src': 'embed', 'start': 3597.634, 'weight': 8, 'content': [{'end': 3605.584, 'text': 'Now population is a collection or a set of individuals or objects or events whose properties are to be analyzed.', 'start': 3597.634, 'duration': 7.95}, {'end': 3611.11, 'text': "So basically you can refer to population as a subject that you're trying to analyze.", 'start': 3606.625, 'duration': 4.485}, {'end': 3616.617, 'text': "Now a sample is just like the word suggests, it's a subset of the population.", 'start': 3611.791, 'duration': 4.826}, {'end': 3622.828, 'text': 'So you have to make sure that you choose the sample in such a way that it represents the entire population.', 'start': 3617.245, 'duration': 5.583}], 'summary': 'Population is a set of individuals to be analyzed. a sample is a subset representing the entire population.', 'duration': 25.194, 'max_score': 3597.634, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY3597634.jpg'}, {'end': 3670.016, 'src': 'embed', 'start': 3644.924, 'weight': 7, 'content': [{'end': 3651.487, 'text': 'Now sampling is a statistical method that deals with the selection of individual observations within a population.', 'start': 3644.924, 'duration': 6.563}, {'end': 3657.53, 'text': 'So sampling is performed in order to infer statistical knowledge about a population.', 'start': 3652.067, 'duration': 5.463}, {'end': 3663.413, 'text': 'If you want to understand the different statistics of a population, like the mean, the median,', 'start': 3658.01, 'duration': 5.403}, {'end': 3670.016, 'text': "the mode or the standard deviation or the variance of a population, then you're going to perform sampling.", 'start': 3663.413, 'duration': 6.603}], 'summary': 'Sampling is used to infer statistical knowledge about a population for understanding its different statistics like mean, median, mode, standard deviation, and variance.', 'duration': 25.092, 'max_score': 3644.924, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY3644924.jpg'}, {'end': 3737.541, 'src': 'embed', 'start': 3708.183, 'weight': 11, 'content': [{'end': 3711.745, 'text': 'well, it might be possible, but this will take forever to do.', 'start': 3708.183, 'duration': 3.562}, {'end': 3712.805, 'text': "now, obviously it's not.", 'start': 3711.745, 'duration': 1.06}, {'end': 3718.088, 'text': "it's not reasonable to go around knocking each door and asking for what does your teenage son eat?", 'start': 3712.805, 'duration': 5.283}, {'end': 3720.949, 'text': 'and all of that all right, this is not very reasonable.', 'start': 3718.088, 'duration': 2.861}, {'end': 3722.41, 'text': "that's why sampling is used.", 'start': 3720.949, 'duration': 1.461}, {'end': 3729.493, 'text': "it's a method wherein a sample of the population is studied in order to draw inference about the entire population.", 'start': 3722.41, 'duration': 7.083}, {'end': 3733.498, 'text': "So it's basically a shortcut to studying the entire population.", 'start': 3730.276, 'duration': 3.222}, {'end': 3737.541, 'text': 'Instead of taking the entire population and finding out all the solutions,', 'start': 3733.879, 'duration': 3.662}], 'summary': 'Sampling is a method to draw inference about the entire population efficiently.', 'duration': 29.358, 'max_score': 3708.183, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY3708183.jpg'}, {'end': 3873.631, 'src': 'embed', 'start': 3827.565, 'weight': 12, 'content': [{'end': 3833.828, 'text': 'Now in this method, each member of the population has an equal chance of being selected in the sample.', 'start': 3827.565, 'duration': 6.263}, {'end': 3842.381, 'text': 'Alright, so each and every individual or each and every object in the population has an equal chance of being a part of the sample.', 'start': 3834.634, 'duration': 7.747}, {'end': 3844.983, 'text': "That's what random sampling is all about.", 'start': 3843.222, 'duration': 1.761}, {'end': 3849.007, 'text': "Okay, you're randomly going to select any individual or any object.", 'start': 3845.323, 'duration': 3.684}, {'end': 3856.181, 'text': 'So this way, each individual has an equal chance of being selected, correct? Next we have systematic sampling.', 'start': 3849.447, 'duration': 6.734}, {'end': 3864.386, 'text': 'Now in systematic sampling, every nth record is chosen from the population to be a part of the sample.', 'start': 3856.741, 'duration': 7.645}, {'end': 3868.288, 'text': "Now refer this image that I've shown over here.", 'start': 3864.406, 'duration': 3.882}, {'end': 3873.631, 'text': 'Out of these six groups, every second group is chosen as a sample.', 'start': 3869.188, 'duration': 4.443}], 'summary': 'Random sampling: equal chance for each member. systematic sampling: every nth record chosen.', 'duration': 46.066, 'max_score': 3827.565, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY3827565.jpg'}, {'end': 3940.291, 'src': 'embed', 'start': 3915.323, 'weight': 14, 'content': [{'end': 3920.687, 'text': 'It is basically a subset of the population that shares at least one common characteristics.', 'start': 3915.323, 'duration': 5.364}, {'end': 3923.369, 'text': 'In our example, it is gender.', 'start': 3921.648, 'duration': 1.721}, {'end': 3931.446, 'text': "So after you've created a stratum, you're going to use random sampling on these stratums and you're going to choose a final sample.", 'start': 3924.002, 'duration': 7.444}, {'end': 3940.291, 'text': 'So random sampling, meaning that all of the individuals in each of the stratum will have an equal chance of being selected in the sample correct?', 'start': 3931.886, 'duration': 8.405}], 'summary': 'Stratified sampling involves creating strata based on common characteristics, like gender, and using random sampling within each stratum to select a final sample.', 'duration': 24.968, 'max_score': 3915.323, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY3915323.jpg'}, {'end': 4024.34, 'src': 'embed', 'start': 3997.96, 'weight': 15, 'content': [{'end': 4007.888, 'text': 'Descriptive statistics is a method which is used to describe and understand the features of specific data set by giving a short summary of the data.', 'start': 3997.96, 'duration': 9.928}, {'end': 4012.051, 'text': 'It is mainly focused upon the characteristics of data.', 'start': 4009.048, 'duration': 3.003}, {'end': 4016.394, 'text': 'It also provides a graphical summary of the data.', 'start': 4012.651, 'duration': 3.743}, {'end': 4024.34, 'text': "In order to make you understand what descriptive statistics is, let's suppose that you want to gift all your classmates a t-shirt.", 'start': 4016.574, 'duration': 7.766}], 'summary': 'Descriptive statistics summarizes features of a dataset, providing graphical summaries and focusing on data characteristics.', 'duration': 26.38, 'max_score': 3997.96, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY3997960.jpg'}, {'end': 4141.063, 'src': 'embed', 'start': 4116.953, 'weight': 16, 'content': [{'end': 4127.738, 'text': 'descriptive statistics is a method that is used to describe and understand the features of a specific data set by giving short summaries about the sample and measures of the data.', 'start': 4116.953, 'duration': 10.785}, {'end': 4131.399, 'text': 'There are two important measures in descriptive statistics.', 'start': 4128.298, 'duration': 3.101}, {'end': 4138.742, 'text': 'We have measure of central tendency, which is also known as measure of center, and we have measures of variability.', 'start': 4131.679, 'duration': 7.063}, {'end': 4141.063, 'text': 'This is also known as measures of spread.', 'start': 4139.102, 'duration': 1.961}], 'summary': 'Descriptive statistics summarize data features using central tendency and variability measures.', 'duration': 24.11, 'max_score': 4116.953, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY4116953.jpg'}], 'start': 2905.736, 'title': 'Statistics and probability in data science', 'summary': 'Covers the essential role of statistics and probability in machine learning, artificial intelligence, and data science, including the agenda for the session and the topics to be covered, such as descriptive statistics, probability, and inferential statistics. it also delves into the fundamentals of data, probability sampling techniques, and using statistics in problem-solving scenarios.', 'chapters': [{'end': 3087.195, 'start': 2905.736, 'title': 'Statistics and probability in data science', 'summary': 'Covers the essential role of statistics and probability in machine learning, artificial intelligence, and data science, including the agenda for the session and the topics to be covered, such as descriptive statistics, probability, and inferential statistics.', 'duration': 181.459, 'highlights': ['The session agenda includes an overview of data, different categories of data, basic terminologies in statistics, types of statistics, descriptive statistics, probability, and inferential statistics. The agenda for the session covers a comprehensive overview of various topics related to statistics and probability, providing a structured plan for the session.', 'Descriptive statistics module will cover measures of center, measures of spread, information gain, entropy, and a discussion on confusion matrix, followed by a demo in the R language. The detailed discussion on descriptive statistics includes specific topics such as measures of center, measures of spread, and a demo in the R language, providing practical application and demonstration of concepts.', 'Probability module will cover terminologies in probability, probability distributions, types of probability, and examples illustrating the different types of probability. The probability module encompasses a range of topics including terminologies, distributions, and practical examples, offering a comprehensive understanding of probability concepts.', 'Inferential statistics module will include point estimation, confidence interval, margin of error, and hypothesis testing, with a focus on practical application through use cases. The section on inferential statistics covers key concepts such as point estimation, confidence interval, and hypothesis testing, emphasizing practical application through use cases.']}, {'end': 3478.429, 'start': 3087.596, 'title': 'Understanding data and statistics', 'summary': 'Covers the fundamentals of data, including its definition, types (qualitative and quantitative), and the role of statistics in data collection, analysis, interpretation, and presentation.', 'duration': 390.833, 'highlights': ['Data is defined as facts and statistics collected for reference or analysis, providing insights for better business decisions, and is divided into qualitative (nominal and ordinal) and quantitative (discrete and continuous) types, each with distinct characteristics and examples.', 'Qualitative data includes nominal (without order or ranking) and ordinal (ordered series of information) data, with examples such as gender (nominal) and customer ratings (ordinal), while quantitative data deals with numbers and can be discrete (finite possible values, e.g., number of students in a class) or continuous (infinite possible values, e.g., weight of a person), with explanations of variable types and examples.', 'Statistics encompasses applied mathematics concerning data collection, analysis, interpretation, and presentation, including methods for visualization and solving complex problems, highlighting the comprehensive nature of the field beyond mere analysis.']}, {'end': 3683.506, 'start': 3479.088, 'title': 'Statistics in problem solving', 'summary': 'Discusses using statistics to solve problems such as testing drug effectiveness, making probability-based decisions, and performing data analysis to improve business performance, with an emphasis on basic terminologies in statistics like population and sample.', 'duration': 204.418, 'highlights': ['The basic idea behind data analysis is to use statistical techniques in order to figure out the relationship between different variables or different components in your business. Data analysis involves using statistical techniques to identify the relationship between variables in a business, aiding in making informed decisions.', 'Sampling is performed in order to infer statistical knowledge about a population and understand statistics like mean, median, mode, standard deviation, and variance. Sampling helps in understanding population statistics such as mean, median, mode, standard deviation, and variance without studying the entire population.', 'Population is a collection of individuals, objects, or events to be analyzed, while a sample is a subset of the population that should represent the entire population to provide accurate information. Understanding the difference between population and sample is crucial in ensuring that a sample accurately represents the entire population for analysis.', 'Statistics can be used to solve problems such as testing drug effectiveness, making probability-based decisions, and performing data analysis to improve business performance. Statistics can be applied to various problems, including testing drug effectiveness, making probability-based decisions, and improving business performance through data analysis.', 'Sampling is a statistical method for the selection of individual observations within a population, allowing inference of statistical knowledge about the population. Sampling is a statistical method that enables inferring statistical knowledge about a population by selecting individual observations within it.']}, {'end': 4411.687, 'start': 3683.506, 'title': 'Probability sampling techniques', 'summary': 'Discusses the use of sampling techniques to study the eating habits of teenagers in the us, emphasizing the need for probability sampling due to the large population size, introduces three types of probability sampling techniques: random sampling, systematic sampling, and stratified sampling, and explains the difference between descriptive and inferential statistics with detailed examples.', 'duration': 728.181, 'highlights': ['Introduction to Probability Sampling Sampling is used to study the eating habits of teenagers in the US due to the large population size, with over 42 million teens, and introduces probability sampling techniques: random sampling, systematic sampling, and stratified sampling.', 'Random Sampling Explains random sampling as a technique where each member of the population has an equal chance of being selected in the sample, ensuring a fair representation of the entire population.', 'Systematic Sampling Describes systematic sampling as a method where every nth record is chosen from the population to be a part of the sample, providing an organized approach to sample selection.', 'Stratified Sampling Introduces stratified sampling as a technique using strata to form samples from a large population, ensuring representation of subgroups with common characteristics and applying random sampling within each stratum.', 'Types of Statistics Distinguishes between descriptive statistics, focusing on characteristics and graphical summaries of data, and inferential statistics, which makes predictions about a population based on a sample, emphasizing the use of probability to draw conclusions.', 'Measures of Central Tendency Explains measures of central tendency including mean, median, and mode, with detailed examples of how these measures are calculated and applied in descriptive statistics.']}], 'duration': 1505.951, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY2905736.jpg', 'highlights': ['The session agenda covers a comprehensive overview of various topics related to statistics and probability, providing a structured plan for the session.', 'Descriptive statistics module includes measures of center, measures of spread, information gain, entropy, and a discussion on confusion matrix, followed by a demo in the R language.', 'Probability module encompasses a range of topics including terminologies, distributions, and practical examples, offering a comprehensive understanding of probability concepts.', 'Inferential statistics module covers key concepts such as point estimation, confidence interval, and hypothesis testing, emphasizing practical application through use cases.', 'Data is defined as facts and statistics collected for reference or analysis, providing insights for better business decisions, and is divided into qualitative (nominal and ordinal) and quantitative (discrete and continuous) types, each with distinct characteristics and examples.', 'Statistics encompasses applied mathematics concerning data collection, analysis, interpretation, and presentation, including methods for visualization and solving complex problems, highlighting the comprehensive nature of the field beyond mere analysis.', 'Data analysis involves using statistical techniques to identify the relationship between variables in a business, aiding in making informed decisions.', 'Sampling helps in understanding population statistics such as mean, median, mode, standard deviation, and variance without studying the entire population.', 'Understanding the difference between population and sample is crucial in ensuring that a sample accurately represents the entire population for analysis.', 'Statistics can be applied to various problems, including testing drug effectiveness, making probability-based decisions, and improving business performance through data analysis.', 'Sampling is a statistical method that enables inferring statistical knowledge about a population by selecting individual observations within it.', 'Introduction to Probability Sampling is used to study the eating habits of teenagers in the US due to the large population size, with over 42 million teens, and introduces probability sampling techniques: random sampling, systematic sampling, and stratified sampling.', 'Explains random sampling as a technique where each member of the population has an equal chance of being selected in the sample, ensuring a fair representation of the entire population.', 'Describes systematic sampling as a method where every nth record is chosen from the population to be a part of the sample, providing an organized approach to sample selection.', 'Introduces stratified sampling as a technique using strata to form samples from a large population, ensuring representation of subgroups with common characteristics and applying random sampling within each stratum.', 'Distinguishes between descriptive statistics, focusing on characteristics and graphical summaries of data, and inferential statistics, which makes predictions about a population based on a sample, emphasizing the use of probability to draw conclusions.', 'Explains measures of central tendency including mean, median, and mode, with detailed examples of how these measures are calculated and applied in descriptive statistics.']}, {'end': 6847.987, 'segs': [{'end': 5072.281, 'src': 'heatmap', 'start': 4723.522, 'weight': 1, 'content': [{'end': 4729.471, 'text': 'Coming to standard deviation is the measure of dispersion of a set of data from its mean.', 'start': 4723.522, 'duration': 5.949}, {'end': 4731.634, 'text': "So it's basically the deviation from your mean.", 'start': 4729.571, 'duration': 2.063}, {'end': 4733.376, 'text': "That's what standard deviation is.", 'start': 4731.814, 'duration': 1.562}, {'end': 4739.625, 'text': "Now to better understand how the measures of spread are calculated, let's look at a small use case.", 'start': 4733.917, 'duration': 5.708}, {'end': 4742.546, 'text': "So let's say Daenerys has 20 dragons.", 'start': 4740.226, 'duration': 2.32}, {'end': 4748.067, 'text': 'They have the numbers nine, two, five, four, and so on, as shown on the screen.', 'start': 4742.947, 'duration': 5.12}, {'end': 4751.208, 'text': 'What you have to do is you have to work out the standard deviation.', 'start': 4748.368, 'duration': 2.84}, {'end': 4756.089, 'text': 'All right, in order to calculate the standard deviation, you need to know the mean right?', 'start': 4751.648, 'duration': 4.441}, {'end': 4759.39, 'text': "So first you're gonna find out the mean of your sample set.", 'start': 4756.569, 'duration': 2.821}, {'end': 4767.451, 'text': 'So how do you calculate the mean? You add all the numbers in your data set and divide it by the total number of samples in your data set.', 'start': 4759.85, 'duration': 7.601}, {'end': 4769.976, 'text': 'So you get a value of seven here.', 'start': 4768.115, 'duration': 1.861}, {'end': 4774.159, 'text': 'Then you calculate the RHS of your standard deviation formula.', 'start': 4770.536, 'duration': 3.623}, {'end': 4778.421, 'text': "So from each data point, you're going to subtract the mean and you're going to square that.", 'start': 4774.719, 'duration': 3.702}, {'end': 4782.143, 'text': "So when you do that, you'll get the following result.", 'start': 4779.241, 'duration': 2.902}, {'end': 4786.766, 'text': "You'll basically get this, 425, 4925, and so on.", 'start': 4782.563, 'duration': 4.203}, {'end': 4790.708, 'text': "So finally, you'll just find the mean of these squared differences.", 'start': 4787.406, 'duration': 3.302}, {'end': 4797.032, 'text': 'So your standard deviation will come up to 2.983 once you take the square root.', 'start': 4791.729, 'duration': 5.303}, {'end': 4798.834, 'text': 'So guys, this is pretty simple.', 'start': 4797.652, 'duration': 1.182}, {'end': 4800.837, 'text': "It's a simple mathematic technique.", 'start': 4798.954, 'duration': 1.883}, {'end': 4807.687, 'text': 'All you have to do is you have to substitute the values in the formula, all right? I hope this was clear to all of you.', 'start': 4800.957, 'duration': 6.73}, {'end': 4814.112, 'text': "Now let's move on and discuss the next topic which is information gain and entropy.", 'start': 4809.23, 'duration': 4.882}, {'end': 4816.793, 'text': 'Now this is one of my favorite topics in statistics.', 'start': 4814.612, 'duration': 2.181}, {'end': 4824.777, 'text': "It's very interesting and this topic is mainly involved in machine learning algorithms like decision trees and random forest.", 'start': 4816.913, 'duration': 7.864}, {'end': 4832.6, 'text': "It's very important for you to know how information gain and entropy really work and why they're so essential in building machine learning models.", 'start': 4825.437, 'duration': 7.163}, {'end': 4836.521, 'text': "We'll focus on the statistic parts of information gain and entropy.", 'start': 4833.3, 'duration': 3.221}, {'end': 4843.162, 'text': "And after that, we'll discuss a use case and see how information gain and entropy is used in decision trees.", 'start': 4837.101, 'duration': 6.061}, {'end': 4848.584, 'text': "So for those of you who don't know what a decision tree is, it is basically a machine learning algorithm.", 'start': 4843.703, 'duration': 4.881}, {'end': 4850.324, 'text': "You don't have to know anything about this.", 'start': 4848.844, 'duration': 1.48}, {'end': 4852.745, 'text': "I'll explain everything in depth, so don't worry.", 'start': 4850.344, 'duration': 2.401}, {'end': 4857.106, 'text': "Now let's look at what exactly entropy and information gain is.", 'start': 4853.185, 'duration': 3.921}, {'end': 4863.678, 'text': 'Now guys entropy is basically the measure of any sort of uncertainty that is present in the data.', 'start': 4858.436, 'duration': 5.242}, {'end': 4867.2, 'text': 'So it can be measured by using this formula.', 'start': 4864.439, 'duration': 2.761}, {'end': 4874.003, 'text': 'So here S is the set of all instances in the data set or all the data items in the data set.', 'start': 4867.56, 'duration': 6.443}, {'end': 4877.305, 'text': 'N is the different type of classes in your data set.', 'start': 4874.463, 'duration': 2.842}, {'end': 4879.666, 'text': 'PI is the event probability.', 'start': 4877.885, 'duration': 1.781}, {'end': 4887.209, 'text': "Now, this might seem a little confusing to you all, but when we go through the use case, you'll understand all of these terms even better.", 'start': 4880.206, 'duration': 7.003}, {'end': 4887.489, 'text': 'all right?', 'start': 4887.209, 'duration': 0.28}, {'end': 4891.21, 'text': 'Coming to information gain, as the word suggests,', 'start': 4887.929, 'duration': 3.281}, {'end': 4899.333, 'text': 'information gain indicates how much information a particular feature or a particular variable gives us about the final outcome.', 'start': 4891.21, 'duration': 8.123}, {'end': 4902.114, 'text': 'Okay, it can be measured by using this formula.', 'start': 4899.813, 'duration': 2.301}, {'end': 4906.995, 'text': 'So again here, H is the entropy of the whole data set S.', 'start': 4902.634, 'duration': 4.361}, {'end': 4911.437, 'text': 'Sj is the number of instances with the j value of an attribute A.', 'start': 4906.995, 'duration': 4.442}, {'end': 4914.037, 'text': 'S is the total number of instances in the data set.', 'start': 4911.437, 'duration': 2.6}, {'end': 4918.979, 'text': 'V is the set of distinct values of an attribute A.', 'start': 4914.337, 'duration': 4.642}, {'end': 4921.5, 'text': 'H is the entropy of subset of instances.', 'start': 4918.979, 'duration': 2.521}, {'end': 4925.841, 'text': 'And H is the entropy of an attribute A.', 'start': 4922.14, 'duration': 3.701}, {'end': 4929.845, 'text': "Even though this seems confusing, I'll clear out the confusion.", 'start': 4926.602, 'duration': 3.243}, {'end': 4938.871, 'text': "Let's discuss a small problem statement where we'll understand how information gain and entropy is used to study the significance of a model.", 'start': 4930.465, 'duration': 8.406}, {'end': 4947.778, 'text': 'So like I said, information gain and entropy are very important statistical measures that let us understand the significance of a predictive model.', 'start': 4939.452, 'duration': 8.326}, {'end': 4951.961, 'text': "To get a more clear understanding, let's look at a use case.", 'start': 4948.738, 'duration': 3.223}, {'end': 4956.58, 'text': "All right, now suppose we're given a problem statement.", 'start': 4953.877, 'duration': 2.703}, {'end': 4963.626, 'text': 'All right, the statement is that you have to predict whether a match can be played or not by studying the weather conditions.', 'start': 4956.96, 'duration': 6.666}, {'end': 4967.57, 'text': 'So the predictor variables here are outlook, humidity, wind.', 'start': 4964.227, 'duration': 3.343}, {'end': 4969.732, 'text': 'Day is also a predictor variable.', 'start': 4968.071, 'duration': 1.661}, {'end': 4972.154, 'text': 'The target variable is basically play.', 'start': 4970.132, 'duration': 2.022}, {'end': 4976.559, 'text': "All right, the target variable is the variable that you're trying to predict.", 'start': 4972.535, 'duration': 4.024}, {'end': 4982.125, 'text': 'okay?. Now, the value of the target variable will decide whether or not a game can be played.', 'start': 4976.559, 'duration': 5.566}, {'end': 4984.887, 'text': "So that's why the play has two values.", 'start': 4982.886, 'duration': 2.001}, {'end': 4985.987, 'text': 'It has no and yes.', 'start': 4984.947, 'duration': 1.04}, {'end': 4990.87, 'text': 'No meaning that the weather conditions are not good and therefore you cannot play the game.', 'start': 4986.548, 'duration': 4.322}, {'end': 4995.873, 'text': 'Yes meaning that the weather conditions are good and suitable for you to play the game.', 'start': 4991.45, 'duration': 4.423}, {'end': 4998.894, 'text': 'So that was a problem statement.', 'start': 4997.313, 'duration': 1.581}, {'end': 5001.135, 'text': 'I hope the problem statement is clear to all of you.', 'start': 4998.994, 'duration': 2.141}, {'end': 5005.858, 'text': 'Now to solve such a problem, we make use of something known as decision trees.', 'start': 5001.776, 'duration': 4.082}, {'end': 5012.012, 'text': 'So guys, think of an inverted tree, and each branch of the tree denotes some decision.', 'start': 5006.528, 'duration': 5.484}, {'end': 5012.293, 'text': 'all right?', 'start': 5012.012, 'duration': 0.281}, {'end': 5015.315, 'text': 'Each branch is known as the branch node,', 'start': 5012.633, 'duration': 2.682}, {'end': 5022.7, 'text': "and at each branch node you're going to take a decision in such a manner that you'll get an outcome at the end of the branch.", 'start': 5015.315, 'duration': 7.385}, {'end': 5023.001, 'text': 'all right?', 'start': 5022.7, 'duration': 0.301}, {'end': 5033.128, 'text': 'Now, this figure here basically shows that out of 14 observations, nine observations result in a yes, meaning that out of 14 days,', 'start': 5023.641, 'duration': 9.487}, {'end': 5035.951, 'text': 'the match can be played only on nine days.', 'start': 5033.128, 'duration': 2.823}, {'end': 5036.191, 'text': 'all right?', 'start': 5035.951, 'duration': 0.24}, {'end': 5043.703, 'text': 'So here, if you see on day one, day two, day eight, day nine and day 11, the outlook has been sunny.', 'start': 5036.94, 'duration': 6.763}, {'end': 5048.765, 'text': "So basically, we're trying to cluster our data set depending on the outlook.", 'start': 5044.623, 'duration': 4.142}, {'end': 5051.906, 'text': 'So when the outlook is sunny, this is our data set.', 'start': 5049.245, 'duration': 2.661}, {'end': 5054.967, 'text': 'When the outlook is overcast, this is what we have.', 'start': 5051.946, 'duration': 3.021}, {'end': 5057.668, 'text': 'And when the outlook is rain, this is what we have.', 'start': 5055.207, 'duration': 2.461}, {'end': 5062.43, 'text': 'So when it is sunny, we have two yeses and three nos.', 'start': 5058.708, 'duration': 3.722}, {'end': 5072.281, 'text': 'When the outlook is overcast, we have all four as yeses, meaning that on the four days when the outlook was overcast, we can play the game.', 'start': 5063.591, 'duration': 8.69}], 'summary': 'Understanding standard deviation and information gain in statistics and machine learning.', 'duration': 348.759, 'max_score': 4723.522, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY4723522.jpg'}, {'end': 5213.455, 'src': 'embed', 'start': 5181.73, 'weight': 1, 'content': [{'end': 5185.573, 'text': "So when it comes to overcast, there's literally no impurity in the data set.", 'start': 5181.73, 'duration': 3.843}, {'end': 5188.195, 'text': 'It is 100% pure subset, right?', 'start': 5185.653, 'duration': 2.542}, {'end': 5192.499, 'text': 'So we want variables like these in order to build a model all right?', 'start': 5188.475, 'duration': 4.024}, {'end': 5197.763, 'text': "Now we don't always get lucky and we don't always find variables that will result in pure subsets.", 'start': 5192.799, 'duration': 4.964}, {'end': 5199.985, 'text': "That's why we have the measure entropy.", 'start': 5198.163, 'duration': 1.822}, {'end': 5205.329, 'text': 'So the lesser the entropy of a particular variable, the more significant that variable will be.', 'start': 5200.425, 'duration': 4.904}, {'end': 5213.455, 'text': 'So in a decision tree, the root node is assigned the best attribute so that the decision tree can predict the most precise outcome.', 'start': 5206.03, 'duration': 7.425}], 'summary': 'Overcast weather results in 100% pure data subset, with entropy guiding variable selection for precise predictions in decision tree models.', 'duration': 31.725, 'max_score': 5181.73, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY5181730.jpg'}, {'end': 5543.055, 'src': 'embed', 'start': 5513.235, 'weight': 0, 'content': [{'end': 5516.277, 'text': "we'll see that for Outlook we have the maximum gain.", 'start': 5513.235, 'duration': 3.042}, {'end': 5521.341, 'text': 'We have 0.247, which is the highest information gain value.', 'start': 5516.858, 'duration': 4.483}, {'end': 5527.305, 'text': 'And you must always choose a variable with the highest information gain to split the data at the root node.', 'start': 5521.981, 'duration': 5.324}, {'end': 5531.868, 'text': "So that's why we assign the Outlook variable at the root node.", 'start': 5528.025, 'duration': 3.843}, {'end': 5534.829, 'text': 'So guys, I hope this use case was clear.', 'start': 5532.867, 'duration': 1.962}, {'end': 5538.171, 'text': 'If any of you have doubts, please keep commenting those doubts.', 'start': 5535.009, 'duration': 3.162}, {'end': 5543.055, 'text': "Now let's move on and look at what exactly a confusion matrix is.", 'start': 5538.692, 'duration': 4.363}], 'summary': 'Outlook has the highest information gain value of 0.247, chosen as root node.', 'duration': 29.82, 'max_score': 5513.235, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY5513235.jpg'}, {'end': 5590.38, 'src': 'embed', 'start': 5562.719, 'weight': 2, 'content': [{'end': 5566.182, 'text': "Now guys, what is a confusion matrix? Now don't get confused.", 'start': 5562.719, 'duration': 3.463}, {'end': 5568.003, 'text': 'This is not any complex topic.', 'start': 5566.202, 'duration': 1.801}, {'end': 5575.028, 'text': 'Now a confusion matrix is a matrix that is often used to describe the performance of a model.', 'start': 5568.483, 'duration': 6.545}, {'end': 5579.852, 'text': 'And this is specifically used for classification models or a classifier.', 'start': 5575.268, 'duration': 4.584}, {'end': 5590.38, 'text': 'And what it does is it will calculate the accuracy or it will calculate the performance of your classifier by comparing your actual results and your predicted results.', 'start': 5580.472, 'duration': 9.908}], 'summary': 'Confusion matrix describes model performance in classification by comparing actual and predicted results.', 'duration': 27.661, 'max_score': 5562.719, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY5562719.jpg'}, {'end': 5908.932, 'src': 'embed', 'start': 5882.579, 'weight': 3, 'content': [{'end': 5887.821, 'text': 'To be more precise, it is the ratio of desired outcome to the total outcomes.', 'start': 5882.579, 'duration': 5.242}, {'end': 5892.022, 'text': 'Now the probability of all outcomes always sum up to one.', 'start': 5888.461, 'duration': 3.561}, {'end': 5894.843, 'text': 'Now the probability will always sum up to one.', 'start': 5892.482, 'duration': 2.361}, {'end': 5897.064, 'text': 'Probability cannot go beyond one.', 'start': 5895.163, 'duration': 1.901}, {'end': 5904.328, 'text': 'Okay, so either your probability can be zero or it can be one, or it can be in the form of decimals like 0.52 or 0.55,', 'start': 5897.802, 'duration': 6.526}, {'end': 5908.932, 'text': 'or it can be in the form of 0.5, 0.7, 0.9, but its value will always stay between the range zero and one.', 'start': 5904.328, 'duration': 4.604}], 'summary': 'Probability is the ratio of desired outcome to the total outcomes, always between 0 and 1.', 'duration': 26.353, 'max_score': 5882.579, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY5882579.jpg'}, {'end': 6324.366, 'src': 'embed', 'start': 6302.69, 'weight': 4, 'content': [{'end': 6311.017, 'text': 'Now, the central limit theory states that the sampling distribution of the mean of any independent random variable will be normal,', 'start': 6302.69, 'duration': 8.327}, {'end': 6313.959, 'text': 'or nearly normal if the sample size is large enough.', 'start': 6311.017, 'duration': 2.942}, {'end': 6315.841, 'text': "Now that's a little confusing.", 'start': 6314.439, 'duration': 1.402}, {'end': 6317.462, 'text': 'Okay, let me break it down for you.', 'start': 6316.161, 'duration': 1.301}, {'end': 6324.366, 'text': 'Now, in simple terms, if we had a large population and we divided it into many samples,', 'start': 6318.042, 'duration': 6.324}], 'summary': 'Central limit theory: sampling mean normal if large samples.', 'duration': 21.676, 'max_score': 6302.69, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY6302690.jpg'}, {'end': 6766.28, 'src': 'embed', 'start': 6737.024, 'weight': 5, 'content': [{'end': 6738.705, 'text': 'So clearly that number is 30.', 'start': 6737.024, 'duration': 1.681}, {'end': 6741.745, 'text': '30 divided by total number of candidates which is 105.', 'start': 6738.705, 'duration': 3.04}, {'end': 6744.746, 'text': 'So here you get the answer clearly.', 'start': 6741.745, 'duration': 3.001}, {'end': 6752.871, 'text': 'Next we have find the probability that a candidate has a good package given that he has not undergone training.', 'start': 6745.346, 'duration': 7.525}, {'end': 6758.835, 'text': "Now this is clearly conditional probability because here you're defining a condition.", 'start': 6753.912, 'duration': 4.923}, {'end': 6766.28, 'text': "You're saying that you want to find the probability of a candidate who has a good package given that he's not undergone any training.", 'start': 6758.895, 'duration': 7.385}], 'summary': 'The probability of a candidate having a good package, given no training, is being calculated with 30 out of 105 candidates.', 'duration': 29.256, 'max_score': 6737.024, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY6737024.jpg'}, {'end': 6824.339, 'src': 'embed', 'start': 6798.387, 'weight': 6, 'content': [{'end': 6804.692, 'text': 'So five divided by 60, you get a probability of around 0.08,, which is pretty low, right?', 'start': 6798.387, 'duration': 6.305}, {'end': 6809.055, 'text': 'Okay, so this was all about the different types of probability.', 'start': 6805.452, 'duration': 3.603}, {'end': 6814.86, 'text': "Now let's move on and look at our last topic in probability, which is Bayes' theorem.", 'start': 6809.796, 'duration': 5.064}, {'end': 6821.138, 'text': 'Now guys, Bayes Theorem is a very important concept when it comes to statistics and probability.', 'start': 6815.655, 'duration': 5.483}, {'end': 6824.339, 'text': 'It is majorly used in naive bias algorithm.', 'start': 6821.638, 'duration': 2.701}], 'summary': "Probability: 5/60 yields 0.08, low. bayes' theorem vital in statistics and naive bias algorithm.", 'duration': 25.952, 'max_score': 6798.387, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY6798387.jpg'}], 'start': 4412.268, 'title': 'Statistics and probability concepts', 'summary': "Explains measures of spread, information gain, entropy, confusion matrix, probability basics, distributions, and types, with examples and calculations, including a decision tree's highest information gain of 0.247 and a confusion matrix evaluating model performance for classification using a sample data of 165 patients.", 'chapters': [{'end': 4800.837, 'start': 4412.268, 'title': 'Measures of spread in statistics', 'summary': 'Explains measures of spread in statistics, including range, interquartile range, variance, and standard deviation, and provides examples and calculations for each measure.', 'duration': 388.569, 'highlights': ['The interquartile range is calculated by subtracting the Q1 from Q3, providing a measure of variability based on dividing a data set into quartiles. The interquartile range (IQR) is a measure of variability based on dividing a data set into quartiles, calculated by subtracting the Q1 from Q3.', 'The range is calculated by subtracting the maximum value in the data set from the minimum value in the data set, providing a measure of how spread apart the values in a data set are. The range is calculated by subtracting the maximum value from the minimum value in the data set, providing a measure of how spread apart the values are.', 'The standard deviation is the measure of dispersion of a set of data from its mean, and its calculation involves finding the mean of the data set and then working out the squared differences from the mean. The standard deviation is the measure of dispersion of a set of data from its mean, and its calculation involves finding the mean of the data set and then working out the squared differences from the mean.', 'Variance is calculated to show how much a random variable differs from its expected value, and it involves computing the squares of deviations from the mean. Variance is calculated to show how much a random variable differs from its expected value, involving computing the squares of deviations from the mean.', 'Population variance and sample variance differ based on whether they are calculated for the entire population data set or a sample data set, with different formulas for each. Population variance and sample variance differ based on whether they are calculated for the entire population data set or a sample data set, with different formulas for each.']}, {'end': 5538.171, 'start': 4800.957, 'title': 'Information gain and entropy in machine learning', 'summary': 'Discusses the concepts of information gain and entropy, their importance in machine learning models, and their application in decision trees through a use case, highlighting that the outlook variable has the highest information gain value of 0.247, making it the best choice for the root node.', 'duration': 737.214, 'highlights': ["The outlook variable has the highest information gain value of 0.247, making it the best choice for the root node in the decision tree. The information gain for the attribute 'outlook' is 0.247, the highest among all variables, indicating its significance as the root node in the decision tree.", 'Entropy is used to measure the impurity or uncertainty of a variable, with the overcast variable resulting in a 100% pure subset, showcasing its significance in building a model. The overcast variable results in a 100% pure subset, indicating its significance in building a model as it has no impurity in the data set.', 'Information gain and entropy help in understanding which variable best splits the data set, with the variable having the highest information gain being chosen for the root node. Information gain and entropy assist in determining the most significant variable for the root node, ensuring the most precise outcome in splitting the data set.']}, {'end': 5811.474, 'start': 5538.692, 'title': 'Understanding confusion matrix', 'summary': 'Explains the concept of a confusion matrix, its use in evaluating model performance for classification, and provides a detailed example of how it calculates accuracy using a sample data of 165 patients, including true positive, true negative, false positive, and false negative cases.', 'duration': 272.782, 'highlights': ['The confusion matrix is used to describe the performance of a model in classification and calculates accuracy by comparing actual and predicted results. It explains the purpose and function of a confusion matrix in evaluating the performance of a classification model.', 'An example is provided using a sample data of 165 patients to demonstrate how the confusion matrix calculates accuracy, including true positive, true negative, false positive, and false negative cases. The transcript provides a detailed example of how the confusion matrix is used to calculate accuracy using a sample data of 165 patients, including the calculation of true positive, true negative, false positive, and false negative cases.', 'The concept of true positive, true negative, false positive, and false negative is explained in detail, providing clarity on their significance in evaluating model performance. It provides a detailed explanation of true positive, true negative, false positive, and false negative, highlighting their significance in evaluating the performance of a model.']}, {'end': 6087.632, 'start': 5811.974, 'title': 'Understanding probability basics', 'summary': 'Discusses the relationship between statistics and probability, the concept of probability as the measure of likelihood of an event, the famous example of rolling a dice, and the terminologies related to probability including random experiment, sample space, and event.', 'duration': 275.658, 'highlights': ['Probability is the measure of how likely an event will occur, represented as the ratio of desired outcome to the total outcomes, always summing up to one. Probability is the measure of how likely an event will occur, represented as the ratio of desired outcome to the total outcomes. The probability of all outcomes always sum up to one.', 'The famous example of probability is rolling a dice, where the probability of getting a specific number, such as three or five, is calculated as the ratio of one outcome to the total outcomes (1/6 in this case). The famous example of probability is rolling a dice, where the probability of getting a specific number, such as three or five, is calculated as the ratio of one outcome to the total outcomes (1/6 in this case).', 'The chapter explains the terminologies related to probability, including random experiment, sample space, and event, and discusses the types of events such as disjoint and non-disjoint events. The chapter explains the terminologies related to probability, including random experiment, sample space, and event, and discusses the types of events such as disjoint and non-disjoint events.']}, {'end': 6410.761, 'start': 6088.955, 'title': 'Probability distributions & central limit theorem', 'summary': 'Discusses probability density function, normal distribution, and central limit theorem, emphasizing the properties and applications of each, with normal distribution and central limit theorem linked to the mean and sampling distribution, demonstrating the behavior of large and small data sets.', 'duration': 321.806, 'highlights': ['The central limit theorem states that the sampling distribution of the mean of any independent random variable will be normal, or nearly normal if the sample size is large enough, with the mean of each sample being almost equal to the mean of the entire population, demonstrating the behavior of large data sets.', 'The normal distribution, also known as the Gaussian distribution, denotes the symmetric property of the mean, where data near the mean occurs more frequently than the data away from the mean, with the mean and standard deviation determining the shape of the bell curve, illustrating the behavior of large and small data sets.', 'The probability density function (PDF) is concerned with the relative likelihood for a continuous random variable to take on a given value, denoting the probability of a variable lying between a specified range, with properties including continuity over a range, the area bounded by the curve equalling one, and the probability value being denoted by the area under the graph, highlighting the behavior of continuous random variables.']}, {'end': 6847.987, 'start': 6411.341, 'title': 'Types of probability and use cases', 'summary': 'Covers the three important types of probability: marginal, joint, and conditional probability, and their application in assessing the salary package and training undergone by candidates, with quantifiable data provided for each type of probability and use case.', 'duration': 436.646, 'highlights': ['Calculating Conditional Probability The explanation and calculation of conditional probability, including the definition and expression for dependent and independent events, with the example of finding the probability of a candidate having a good package given that they have not undergone training.', "Calculating Joint Probability The explanation and calculation of joint probability, with the example of finding the probability of a candidate having attended Edureka's training and also having a good package, providing quantifiable data and a clear methodology for the calculation.", "Calculating Marginal Probability The explanation and calculation of marginal probability, with the example of finding the probability that a candidate has undergone Edureka's training, providing quantifiable data and a clear methodology for the calculation.", "Application of Bayes' Theorem Introduction to Bayes' Theorem, its importance in statistics and probability, and its application in supervised learning classification algorithms such as naive bias, with a real-world example of its use in Gmail spam filtering."]}], 'duration': 2435.719, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY4412268.jpg', 'highlights': ['The outlook variable has the highest information gain value of 0.247, making it the best choice for the root node in the decision tree.', 'The overcast variable results in a 100% pure subset, indicating its significance in building a model as it has no impurity in the data set.', 'The confusion matrix is used to describe the performance of a model in classification and calculates accuracy by comparing actual and predicted results.', 'Probability is the measure of how likely an event will occur, represented as the ratio of desired outcome to the total outcomes, always summing up to one.', 'The central limit theorem states that the sampling distribution of the mean of any independent random variable will be normal, or nearly normal if the sample size is large enough, with the mean of each sample being almost equal to the mean of the entire population, demonstrating the behavior of large data sets.', 'The explanation and calculation of conditional probability, including the definition and expression for dependent and independent events, with the example of finding the probability of a candidate having a good package given that they have not undergone training.', "Introduction to Bayes' Theorem, its importance in statistics and probability, and its application in supervised learning classification algorithms such as naive bias, with a real-world example of its use in Gmail spam filtering."]}, {'end': 8068.351, 'segs': [{'end': 6897.333, 'src': 'embed', 'start': 6848.768, 'weight': 0, 'content': [{'end': 6853.049, 'text': "So now let's discuss what exactly the Bayes Theorem is and what it denotes.", 'start': 6848.768, 'duration': 4.281}, {'end': 6859.811, 'text': 'The Bayes Theorem is used to show the relation between one conditional probability and its inverse.', 'start': 6854.089, 'duration': 5.722}, {'end': 6869.557, 'text': "Basically, it's nothing but the probability of an event occurring based on prior knowledge of conditions that might be related to the same event.", 'start': 6861.312, 'duration': 8.245}, {'end': 6876.081, 'text': 'Mathematically, the Bayes theorem is represented like this, like shown in this equation.', 'start': 6870.678, 'duration': 5.403}, {'end': 6885.186, 'text': 'The left-hand term is referred to as the likelihood ratio, which measures the probability of occurrence of event B given an event A.', 'start': 6876.821, 'duration': 8.365}, {'end': 6889.328, 'text': 'On the left hand side is what is known as the posterior.', 'start': 6886.467, 'duration': 2.861}, {'end': 6897.333, 'text': 'It is referred to as posterior which means that the probability of occurrence of A given an event B.', 'start': 6889.868, 'duration': 7.465}], 'summary': 'Bayes theorem shows relation between conditional probabilities and is represented mathematically with likelihood ratio and posterior.', 'duration': 48.565, 'max_score': 6848.768, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY6848768.jpg'}, {'end': 7012.957, 'src': 'embed', 'start': 6985.95, 'weight': 3, 'content': [{'end': 6989.312, 'text': "So first of all, what we'll do is let's consider A.", 'start': 6985.95, 'duration': 3.362}, {'end': 6993.695, 'text': 'Let A be the event of picking a blue ball from bag A.', 'start': 6989.312, 'duration': 4.383}, {'end': 6997.117, 'text': 'And let X be the event of picking exactly two blue balls.', 'start': 6993.695, 'duration': 3.422}, {'end': 7001.159, 'text': 'Because these are the two events that we need to calculate the probability of.', 'start': 6997.817, 'duration': 3.342}, {'end': 7004.363, 'text': 'Now there are two probabilities that you need to consider here.', 'start': 7001.719, 'duration': 2.644}, {'end': 7012.957, 'text': 'One is the event of picking a blue ball from bag A and the other is the event of picking exactly two blue balls.', 'start': 7004.884, 'duration': 8.073}], 'summary': 'Calculate the probability of picking a blue ball from bag a and exactly two blue balls.', 'duration': 27.007, 'max_score': 6985.95, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY6985950.jpg'}, {'end': 7107.452, 'src': 'embed', 'start': 7081.164, 'weight': 1, 'content': [{'end': 7086.269, 'text': "So you'll pick one blue ball from bowl A and one from bowl B.", 'start': 7081.164, 'duration': 5.105}, {'end': 7091.154, 'text': 'In the second case, you can pick one from A and another blue ball from C.', 'start': 7086.269, 'duration': 4.885}, {'end': 7096.059, 'text': 'In the third case, you can pick a blue ball from bag B and a blue ball from bag C.', 'start': 7091.154, 'duration': 4.905}, {'end': 7101.645, 'text': 'These are the three ways in which it is possible, so you need to find the probability of each of this.', 'start': 7096.98, 'duration': 4.665}, {'end': 7107.452, 'text': 'Step two is that you need to find the probability of A and X occurring together.', 'start': 7102.266, 'duration': 5.186}], 'summary': 'Calculate probabilities of picking blue balls from different bowls and bags, and finding the probability of two specific events occurring together.', 'duration': 26.288, 'max_score': 7081.164, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY7081164.jpg'}, {'end': 7231.976, 'src': 'embed', 'start': 7212.193, 'weight': 5, 'content': [{'end': 7223.758, 'text': 'Point estimation is concerned with the use of the sample data to measure a single value which serves as an approximate value or the best estimate of an unknown population parameter.', 'start': 7212.193, 'duration': 11.565}, {'end': 7225.338, 'text': "That's a little confusing.", 'start': 7224.278, 'duration': 1.06}, {'end': 7226.779, 'text': 'Let me break it down to you.', 'start': 7225.378, 'duration': 1.401}, {'end': 7231.976, 'text': 'For example, in order to calculate the mean of a huge population.', 'start': 7227.429, 'duration': 4.547}], 'summary': 'Point estimation uses sample data to estimate population parameter, e.g. calculating mean.', 'duration': 19.783, 'max_score': 7212.193, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY7212193.jpg'}, {'end': 7397.552, 'src': 'embed', 'start': 7367.673, 'weight': 6, 'content': [{'end': 7371.017, 'text': 'this is where confidence interval also comes into the picture right?', 'start': 7367.673, 'duration': 3.344}, {'end': 7375.66, 'text': 'Apart from interval estimation, we also have something known as margin of error.', 'start': 7371.718, 'duration': 3.942}, {'end': 7378.682, 'text': "So I'll be discussing all of this in the upcoming slides.", 'start': 7375.96, 'duration': 2.722}, {'end': 7382.103, 'text': "So first let's understand what is interval estimate.", 'start': 7379.162, 'duration': 2.941}, {'end': 7390.388, 'text': 'An interval or range of values which are used to estimate a population parameter is known as an interval estimation.', 'start': 7383.184, 'duration': 7.204}, {'end': 7392.149, 'text': "That's very understandable.", 'start': 7390.928, 'duration': 1.221}, {'end': 7397.552, 'text': "Basically what they're trying to say is you're going to estimate the value of a parameter.", 'start': 7392.429, 'duration': 5.123}], 'summary': 'Interval estimation and margin of error are discussed in relation to population parameter estimation.', 'duration': 29.879, 'max_score': 7367.673, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY7367673.jpg'}, {'end': 7518.039, 'src': 'embed', 'start': 7495.951, 'weight': 7, 'content': [{'end': 7506.435, 'text': 'Confidence interval is the measure of your confidence that the interval estimated contains the population parameter or the population mean or any of those parameters.', 'start': 7495.951, 'duration': 10.484}, {'end': 7515.859, 'text': 'Now statisticians use confidence interval to describe the amount of uncertainty associated with a sample estimate of a population parameter.', 'start': 7507.435, 'duration': 8.424}, {'end': 7518.039, 'text': 'Now guys, this is a lot of definition.', 'start': 7516.359, 'duration': 1.68}], 'summary': 'Confidence interval measures uncertainty in sample estimates of population parameters.', 'duration': 22.088, 'max_score': 7495.951, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY7495951.jpg'}, {'end': 7603.124, 'src': 'embed', 'start': 7580.177, 'weight': 8, 'content': [{'end': 7590.34, 'text': 'Now, margin of error for a given level of confidence is the greatest possible distance between the point estimate and the value of the parameter that it is estimating.', 'start': 7580.177, 'duration': 10.163}, {'end': 7595.222, 'text': 'You can say that it is a deviation from the actual point estimate.', 'start': 7590.92, 'duration': 4.302}, {'end': 7598.623, 'text': 'Now the margin of error can be calculated using this formula.', 'start': 7595.362, 'duration': 3.261}, {'end': 7603.124, 'text': 'Now ZC here denotes the critical value or the confidence interval.', 'start': 7599.103, 'duration': 4.021}], 'summary': 'Margin of error is the greatest possible distance between point estimate and parameter value, calculated using a formula with zc as the critical value.', 'duration': 22.947, 'max_score': 7580.177, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY7580177.jpg'}, {'end': 8068.351, 'src': 'embed', 'start': 8044.018, 'weight': 9, 'content': [{'end': 8051.379, 'text': 'Therefore, in our example, if the probability of an event occurring is less than 5%, which it is, then the event is biased.', 'start': 8044.018, 'duration': 7.361}, {'end': 8053.96, 'text': 'Hence, it proves the alternate hypothesis.', 'start': 8051.779, 'duration': 2.181}, {'end': 8058.007, 'text': 'So guys, with this, we come to the end of the session.', 'start': 8054.625, 'duration': 3.382}, {'end': 8068.351, 'text': 'Since we have already learned how statistics and probability act as backbone of data science,', 'start': 8063.429, 'duration': 4.922}], 'summary': 'Probability < 5% indicates bias, proving alternate hypothesis. statistics and probability are crucial in data science.', 'duration': 24.333, 'max_score': 8044.018, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY8044018.jpg'}], 'start': 6848.768, 'title': 'Probability and estimation', 'summary': 'Explains bayes theorem and conditional probability, focusing on calculating conditional probabilities and discussing point estimation, interval estimation, confidence interval, margin of error, and hypothesis testing, encouraging audience participation.', 'chapters': [{'end': 6985.39, 'start': 6848.768, 'title': 'Understanding bayes theorem', 'summary': 'Explains the concept of bayes theorem, its mathematical representation, and provides an example to demonstrate its application in calculating conditional probabilities, encouraging audience participation.', 'duration': 136.622, 'highlights': ['Explanation of Bayes Theorem and its mathematical representation The Bayes Theorem is discussed, along with its mathematical representation and the meaning of key terms like likelihood ratio, posterior, and prior.', 'Example of applying Bayes Theorem to calculate conditional probabilities A specific example is provided involving three bowls with different colored balls, prompting the audience to calculate the probability of drawing a blue ball from a specific bowl given the total number of blue balls drawn.', 'Encouragement for audience participation in solving the example problem The audience is encouraged to solve the example problem using the provided formula and steps, aiming to engage them in applying the Bayes Theorem concept.']}, {'end': 7189.232, 'start': 6985.95, 'title': 'Conditional probability calculation', 'summary': 'Discusses the calculation of conditional probability, specifically focusing on the probability of picking a blue ball from bag a given that exactly two blue balls are picked, and the ways to calculate the probabilities are explained.', 'duration': 203.282, 'highlights': ['The chapter explains the calculation of the probability of picking a blue ball from bag A given that exactly two blue balls are picked, using the definition of conditional probability and the formula for the occurrence of event A given an event X.', 'It outlines the three ways to calculate the probability of picking exactly two blue balls: picking one blue ball from bag A and one from bag B, one from A and another from bag C, and one from bag B and one from bag C.', 'The chapter introduces the concept of inferential statistics as the second type of statistics, following the completion of the probability module and the earlier discussion on descriptive statistics.']}, {'end': 7475.035, 'start': 7189.232, 'title': 'Point estimation and interval estimation', 'summary': 'Explains point estimation, which involves using sample data to estimate a single value of an unknown population parameter, and interval estimation, where a range of values is used to estimate a population parameter, with various methods like method of moments, maximum of likelihood, base estimator, and best unbiased estimators discussed.', 'duration': 285.803, 'highlights': ['Point estimation involves using sample data to estimate a single value of an unknown population parameter, such as the population mean, and is a fundamental concept in statistics. Point estimation is the process of using sample data to approximate a single value for an unknown population parameter, such as the population mean, and is essential in statistical analysis.', 'Interval estimation is a method used to estimate a population parameter by creating a range of values within which the parameter is likely to lie, providing a more accurate prediction compared to point estimation. Interval estimation involves creating a range of values to estimate a population parameter, like the population mean, providing a more accurate prediction compared to point estimation, as it accounts for the uncertainty in the estimate.', 'Method of moments, maximum of likelihood, base estimator, and best unbiased estimators are common methods used to find estimates in point estimation. Method of moments, maximum of likelihood, base estimator, and best unbiased estimators are common methods used to find estimates in point estimation, each with its own approach to approximating population parameters.']}, {'end': 8068.351, 'start': 7475.576, 'title': 'Confidence interval & margin of error', 'summary': 'Covers the concepts of confidence interval and margin of error, providing examples and formulas, and then delves into hypothesis testing using a real-life scenario to explain probability and hypothesis testing.', 'duration': 592.775, 'highlights': ['Confidence interval measures the uncertainty associated with a sample estimate of a population parameter, with a 99% confidence level indicating a high level of confidence in the results. Confidence interval measures the uncertainty associated with a sample estimate of a population parameter, with a 99% confidence level indicating a high level of confidence in the results.', 'Margin of error is the greatest possible distance between the point estimate and the value of the parameter being estimated, and it can be calculated using a formula involving the critical value, standard deviation, and sample size. Margin of error is the greatest possible distance between the point estimate and the value of the parameter being estimated, and it can be calculated using a formula involving the critical value, standard deviation, and sample size.', 'Explanation of hypothesis testing using a real-life scenario involving probability calculations and defining the threshold value to determine the bias in the event. Explanation of hypothesis testing using a real-life scenario involving probability calculations and defining the threshold value to determine the bias in the event.']}], 'duration': 1219.583, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY6848768.jpg', 'highlights': ['The Bayes Theorem is discussed, along with its mathematical representation and the meaning of key terms like likelihood ratio, posterior, and prior.', 'A specific example is provided involving three bowls with different colored balls, prompting the audience to calculate the probability of drawing a blue ball from a specific bowl given the total number of blue balls drawn.', 'The audience is encouraged to solve the example problem using the provided formula and steps, aiming to engage them in applying the Bayes Theorem concept.', 'The chapter explains the calculation of the probability of picking a blue ball from bag A given that exactly two blue balls are picked, using the definition of conditional probability and the formula for the occurrence of event A given an event X.', 'It outlines the three ways to calculate the probability of picking exactly two blue balls: picking one blue ball from bag A and one from bag B, one from A and another from bag C, and one from bag B and one from bag C.', 'Point estimation involves using sample data to estimate a single value of an unknown population parameter, such as the population mean, and is a fundamental concept in statistics.', 'Interval estimation is a method used to estimate a population parameter by creating a range of values within which the parameter is likely to lie, providing a more accurate prediction compared to point estimation.', 'Confidence interval measures the uncertainty associated with a sample estimate of a population parameter, with a 99% confidence level indicating a high level of confidence in the results.', 'Margin of error is the greatest possible distance between the point estimate and the value of the parameter being estimated, and it can be calculated using a formula involving the critical value, standard deviation, and sample size.', 'Explanation of hypothesis testing using a real-life scenario involving probability calculations and defining the threshold value to determine the bias in the event.']}, {'end': 9872.596, 'segs': [{'end': 8308.101, 'src': 'embed', 'start': 8280.63, 'weight': 0, 'content': [{'end': 8287.316, 'text': 'Then it is actually pretty fast when you compare it with lists and at the same time it is very convenient to work with NumPy.', 'start': 8280.63, 'duration': 6.686}, {'end': 8294.001, 'text': 'So these are the three major advantages that NumPy has over lists and that is the reason why we use NumPy instead of lists.', 'start': 8287.716, 'duration': 6.285}, {'end': 8298.138, 'text': "Now don't worry, I'm actually going to prove it to you practically by opening my PyCharm.", 'start': 8294.537, 'duration': 3.601}, {'end': 8300.339, 'text': 'So guys, this is my PyCharm again.', 'start': 8298.878, 'duration': 1.461}, {'end': 8308.101, 'text': 'So first thing that I need to do is import numpy as np.', 'start': 8300.359, 'duration': 7.742}], 'summary': 'Numpy is faster and more convenient than lists, proven practically by opening pycharm.', 'duration': 27.471, 'max_score': 8280.63, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY8280629.jpg'}, {'end': 8462.565, 'src': 'embed', 'start': 8437.509, 'weight': 1, 'content': [{'end': 8444.574, 'text': 'So this actually shows the memory that has been occupied by my list and this shows the memory that has been occupied by my numpy array.', 'start': 8437.509, 'duration': 7.065}, {'end': 8447.996, 'text': 'So as you can see there is quite a lot of difference between both of them.', 'start': 8444.974, 'duration': 3.022}, {'end': 8451.778, 'text': 'So we have proved our first point that it actually occupies less memory.', 'start': 8448.416, 'duration': 3.362}, {'end': 8456.401, 'text': 'Now when I talk about numpy array is faster and more convenient than the list.', 'start': 8451.938, 'duration': 4.463}, {'end': 8462.565, 'text': "So the next step is I'm going to prove it to you that numpy array is actually faster and more convenient than list.", 'start': 8456.661, 'duration': 5.904}], 'summary': 'Numpy array occupies less memory and is faster than list.', 'duration': 25.056, 'max_score': 8437.509, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY8437509.jpg'}, {'end': 8733.633, 'src': 'embed', 'start': 8707.999, 'weight': 2, 'content': [{'end': 8712.741, 'text': 'So you can find the dimension of your array, whether it is a two dimensional or a single dimensional array.', 'start': 8707.999, 'duration': 4.742}, {'end': 8715.182, 'text': 'then you can even calculate the byte size of each element.', 'start': 8712.741, 'duration': 2.441}, {'end': 8716.189, 'text': 'It is pretty easy.', 'start': 8715.529, 'duration': 0.66}, {'end': 8719.05, 'text': "I'm going to tell you that practically you don't need to worry about that,", 'start': 8716.249, 'duration': 2.801}, {'end': 8721.93, 'text': 'and you can even find the data types of the elements that are stored in your array.', 'start': 8719.05, 'duration': 2.88}, {'end': 8725.571, 'text': 'So if you want to know what is the data type of the elements you can do that as well.', 'start': 8721.95, 'duration': 3.621}, {'end': 8729.192, 'text': "So let me show you these three operations first and then we'll move forward to the other operations.", 'start': 8725.791, 'duration': 3.401}, {'end': 8731.572, 'text': "I'm going to open my pie charm once more guys.", 'start': 8729.552, 'duration': 2.02}, {'end': 8733.633, 'text': 'Let me remove all of this.', 'start': 8732.412, 'duration': 1.221}], 'summary': 'Demonstrating array operations, including dimension, byte size, and data types.', 'duration': 25.634, 'max_score': 8707.999, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY8707999.jpg'}, {'end': 9464.321, 'src': 'embed', 'start': 9428.407, 'weight': 3, 'content': [{'end': 9430.188, 'text': 'So this is how you find standard deviation.', 'start': 9428.407, 'duration': 1.781}, {'end': 9433.949, 'text': 'Now let us go back to our slides and see what are the other operations that are still left.', 'start': 9430.268, 'duration': 3.681}, {'end': 9440.724, 'text': 'Now, these are the basic mathematical functions that you can perform with numpy arrays addition, multiplication,', 'start': 9434.649, 'duration': 6.075}, {'end': 9444.287, 'text': "subtraction and division and that'll actually happen element-wise.", 'start': 9440.724, 'duration': 3.563}, {'end': 9451.432, 'text': 'So basically you are performing matrix addition, matrix multiplication, matrix division, as well as matrix subtraction.', 'start': 9444.727, 'duration': 6.705}, {'end': 9453.694, 'text': 'Let me go ahead and show it to you practically.', 'start': 9451.772, 'duration': 1.922}, {'end': 9455.975, 'text': 'It is very, very simple, guys.', 'start': 9453.714, 'duration': 2.261}, {'end': 9464.321, 'text': "So similarly, I'm going to define one more array, and let me name it as B.", 'start': 9456.796, 'duration': 7.525}], 'summary': 'Introduction to performing basic mathematical functions with numpy arrays.', 'duration': 35.914, 'max_score': 9428.407, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY9428407.jpg'}, {'end': 9789.301, 'src': 'embed', 'start': 9759.1, 'weight': 4, 'content': [{'end': 9766.746, 'text': "So when I talk about log it is actually log base 10 and when I'm talking about natural log that is log base e I will write it as ln.", 'start': 9759.1, 'duration': 7.646}, {'end': 9770.175, 'text': "So instead of that I've written log, that means log base 10.", 'start': 9767.014, 'duration': 3.161}, {'end': 9773.096, 'text': 'So you can perform these operations with the help of numpy.', 'start': 9770.175, 'duration': 2.921}, {'end': 9774.996, 'text': 'Let me show you how you can do that.', 'start': 9773.576, 'duration': 1.42}, {'end': 9789.301, 'text': "So I'll open my PyCharm, let me remove this, and I'm going to define a numpy array, let it be ar equal to np.array, one comma two comma three.", 'start': 9775.977, 'duration': 13.324}], 'summary': 'Using numpy, log operations can be performed on arrays in python.', 'duration': 30.201, 'max_score': 9759.1, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY9759100.jpg'}], 'start': 8068.351, 'title': 'Introduction to numpy in python', 'summary': 'Introduces the numpy module in python, emphasizing its memory efficiency, speed, and convenience over lists, with specific examples. it demonstrates the differences in memory usage and performance between lists and numpy arrays, covers various operations, mathematical operations, and special functions with practical demonstrations.', 'chapters': [{'end': 8300.339, 'start': 8068.351, 'title': 'Introduction to numpy in python', 'summary': 'Introduces the numpy module in python, highlighting its functionalities, advantages over lists, and practical demonstrations, emphasizing its memory efficiency, speed, and convenience, with specific examples and comparisons.', 'duration': 231.988, 'highlights': ['Numpy offers advantages over lists due to its memory efficiency, speed, and convenience, making it a preferred choice for data science applications. (3 advantages highlighted)', 'Numpy contains n-dimensional array objects, tools for integrating with C, C++, and is useful in linear algebra, Fourier transform, and random number capabilities.', 'Numpy arrays can be created using the numpy module in Python, and the process is demonstrated practically using PyCharm, including the creation of single-dimensional and two-dimensional arrays.', 'The process of installing the numpy module in PyCharm is explained, including the steps to import the module and create arrays, with a practical demonstration of array creation.', 'An explanation and visual depiction of multi-dimensional arrays, clarifying the concept of rows and columns and its representation as a matrix, with a practical demonstration of creating a two-dimensional array using numpy in PyCharm.']}, {'end': 8705.438, 'start': 8300.359, 'title': 'Comparing list and numpy array performance', 'summary': 'Demonstrates the differences in memory usage and performance between lists and numpy arrays, showing that numpy arrays occupy less memory and are faster and more convenient than lists, with a significant performance difference in computing the sum.', 'duration': 405.079, 'highlights': ['NumPy arrays occupy less memory than lists The chapter demonstrates that a NumPy array occupies less memory than a list, with the memory occupied by the NumPy array being significantly lower than that of the list.', 'NumPy arrays are faster and more convenient than lists The chapter shows that NumPy arrays are both faster and more convenient than lists, with the computing time for the sum of a NumPy array being significantly lower than that of a list.']}, {'end': 9341.106, 'start': 8707.999, 'title': 'Numpy array operations', 'summary': 'Covers various operations on numpy arrays including finding dimensions, byte size, data type, size, shape, reshape, slicing, line spacing, minimum, maximum, sum, and axis concept, with practical demonstrations and examples.', 'duration': 633.107, 'highlights': ['You can find the dimension, byte size, and data type of elements in a numpy array. The ability to determine the dimension, byte size, and data type of elements in a numpy array provides insights into the structure and memory consumption of the array.', 'You can find the size and shape of a numpy array, as well as perform reshape and slicing operations. Understanding the size, shape, reshape, and slicing operations of a numpy array is crucial for manipulating and extracting specific elements from the array.', 'Performing operations like finding minimum, maximum, sum, and understanding the axis concept in numpy arrays. Calculating the minimum, maximum, sum, and understanding the axis concept allows for statistical analysis and manipulation of numpy arrays.', 'Demonstration of line spacing operation in numpy arrays. Understanding line spacing in numpy arrays enables the generation of equally spaced values within a specified range.']}, {'end': 9599.948, 'start': 9341.106, 'title': 'Numpy mathematical operations and array stacking', 'summary': 'Covers basic mathematical operations using numpy arrays including finding square root, standard deviation, element-wise addition, subtraction, multiplication, division, and array stacking both vertically and horizontally.', 'duration': 258.842, 'highlights': ['The chapter covers basic mathematical operations using numpy arrays including finding square root, standard deviation, element-wise addition, subtraction, multiplication, division, and array stacking both vertically and horizontally.', 'Numpy allows performing operations like finding square root and standard deviation on arrays to obtain specific results, such as square root of each element and how much each element varies from the mean value of the array.', 'Element-wise addition, subtraction, multiplication, and division can be performed on numpy arrays, resulting in operations like 1 plus 1 is 2, 2 plus 2 is 4, 3 plus 3 is 6, 1 minus 1 is 0, 2 minus 2 is 0, 3 minus 3 is 0, 1 into 1 is 1, 2 into 2 is 4, 3 into 3 is 9, 1 divided by 1 is 1, 2 divided by 2 is 1, 3 divided by 3 is 1.', 'Numpy also offers array stacking, allowing for vertical stacking and horizontal stacking of arrays, with specific examples provided for each.']}, {'end': 9872.596, 'start': 9600.308, 'title': 'Numpy array operations and special functions', 'summary': 'Covers transforming a numpy array to a single column, plotting sine and cosine graphs using matplotlib, and performing exponential and logarithmic calculations with numpy.', 'duration': 272.288, 'highlights': ["Transforming a numpy array to a single column The process involves using the 'print A.ravel()' command, resulting in a single column output of the numpy array.", 'Plotting sine and cosine graphs using matplotlib The speaker demonstrates importing matplotlib.pyplot as plt, defining x and y coordinates, and using plt to plot the graph, showcasing the sine and cosine graphs.', "Performing exponential and logarithmic calculations with numpy The process includes defining a numpy array, calculating the exponential value using 'np.array exp', and finding natural log and log base 10 values using 'log' and '10' respectively."]}], 'duration': 1804.245, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY8068351.jpg', 'highlights': ['Numpy offers advantages over lists due to its memory efficiency, speed, and convenience, making it a preferred choice for data science applications.', 'Numpy arrays occupy less memory than lists, with the memory occupied by the NumPy array being significantly lower than that of the list.', 'You can find the dimension, byte size, and data type of elements in a numpy array, providing insights into the structure and memory consumption of the array.', 'The chapter covers basic mathematical operations using numpy arrays including finding square root, standard deviation, element-wise addition, subtraction, multiplication, division, and array stacking both vertically and horizontally.', 'Performing exponential and logarithmic calculations with numpy, including defining a numpy array, calculating the exponential value, and finding natural log and log base 10 values.']}, {'end': 11698.365, 'segs': [{'end': 9943.515, 'src': 'embed', 'start': 9914.076, 'weight': 1, 'content': [{'end': 9917.298, 'text': "So we'll move forward and we'll see what are the various applications of Python.", 'start': 9914.076, 'duration': 3.222}, {'end': 9920.94, 'text': 'So these are the applications of Python.', 'start': 9919.359, 'duration': 1.581}, {'end': 9923.662, 'text': 'I have listed only four of those although there are many more.', 'start': 9921.161, 'duration': 2.501}, {'end': 9929.786, 'text': 'So you can perform web scraping with Python that is you can extract certain contents from a particular web page.', 'start': 9924.443, 'duration': 5.343}, {'end': 9931.787, 'text': 'You can perform a web development.', 'start': 9930.286, 'duration': 1.501}, {'end': 9935.069, 'text': 'You can perform testing as well as you can perform data analysis.', 'start': 9931.867, 'duration': 3.202}, {'end': 9939.272, 'text': "So for today's session we'll be focusing on a data analysis part of Python.", 'start': 9935.47, 'duration': 3.802}, {'end': 9943.515, 'text': 'So guys let us move forward and see what exactly is data lifecycle.', 'start': 9940.093, 'duration': 3.422}], 'summary': "Python has various applications including web scraping, web development, testing, and data analysis. this session focuses on python's data analysis capabilities.", 'duration': 29.439, 'max_score': 9914.076, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY9914076.jpg'}, {'end': 10013.703, 'src': 'embed', 'start': 9974.466, 'weight': 2, 'content': [{'end': 9977.008, 'text': "So various other things that we are going to see in today's session.", 'start': 9974.466, 'duration': 2.542}, {'end': 9984.352, 'text': 'Now, once you have done the analysis, you can even plot it in the form of a graph and that stage is called a data visualization.', 'start': 9977.908, 'duration': 6.444}, {'end': 9987.493, 'text': 'So this is just a general overview about data lifecycle.', 'start': 9984.752, 'duration': 2.741}, {'end': 9991.896, 'text': "So let's move forward and understand what exactly is data analysis.", 'start': 9988.034, 'duration': 3.862}, {'end': 9999.62, 'text': 'So what is data analysis? So let us understand data analysis with the help of an example that is there in front of your screen over here.', 'start': 9993.257, 'duration': 6.363}, {'end': 10001.041, 'text': 'What happens we have a data set.', 'start': 9999.64, 'duration': 1.401}, {'end': 10005.259, 'text': 'in which we have data about the unemployed youth across the globe.', 'start': 10001.638, 'duration': 3.621}, {'end': 10013.703, 'text': 'So country wise from 2010 to 2014 the percentage of youth that is unemployed within that particular country we have data about that.', 'start': 10005.74, 'duration': 7.963}], 'summary': 'Overview of data lifecycle, data analysis, and data visualization using example of unemployed youth data from 2010 to 2014.', 'duration': 39.237, 'max_score': 9974.466, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY9974466.jpg'}, {'end': 10079.463, 'src': 'embed', 'start': 10054.547, 'weight': 0, 'content': [{'end': 10060.011, 'text': 'So basically to perform data analysis with Python you need to import a particular module which is called Pandas.', 'start': 10054.547, 'duration': 5.464}, {'end': 10063.254, 'text': 'So let us discuss about Pandas in the upcoming slides.', 'start': 10060.392, 'duration': 2.862}, {'end': 10073.182, 'text': 'What is Pandas? Pandas is a software module written for Python programming language which is used for data manipulation and data analysis.', 'start': 10065.075, 'duration': 8.107}, {'end': 10079.463, 'text': 'Now it can perform that at a fairly high performance rate when it is compared to other Python procedures.', 'start': 10073.962, 'duration': 5.501}], 'summary': 'Python data analysis using pandas for high performance data manipulation.', 'duration': 24.916, 'max_score': 10054.547, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY10054547.jpg'}, {'end': 10312.888, 'src': 'embed', 'start': 10287.987, 'weight': 3, 'content': [{'end': 10294.234, 'text': 'So this is a very basic introductory example for you all guys in order to show you how to make data frames using pandas library.', 'start': 10287.987, 'duration': 6.247}, {'end': 10300.722, 'text': "So I'll open my slides and we'll move forward and have a look at various operations that you can perform on pandas data frame.", 'start': 10295.055, 'duration': 5.667}, {'end': 10305.501, 'text': 'So these are the operations that you can perform with Pandas data frame.', 'start': 10302.639, 'duration': 2.862}, {'end': 10307.303, 'text': 'You can slice the data frame.', 'start': 10305.922, 'duration': 1.381}, {'end': 10310.966, 'text': 'That is if you want only a particular part of that data frame, you can do that.', 'start': 10307.323, 'duration': 3.643}, {'end': 10312.888, 'text': 'You can change the index value.', 'start': 10311.326, 'duration': 1.562}], 'summary': 'Intro to using pandas library for data frames and operations.', 'duration': 24.901, 'max_score': 10287.987, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY10287987.jpg'}], 'start': 9873.136, 'title': 'Python data analysis and pandas operations', 'summary': 'Covers python data analysis applications, data lifecycle, and pandas operations, including data frame creation, basic operations, and practical examples of performing data operations with pandas. additionally, it includes a specific data analysis on global youth unemployment, showing percentage changes for various countries from 2010 to 2011 and visualizing the findings through a bar plot.', 'chapters': [{'end': 9931.787, 'start': 9873.136, 'title': 'Python data analysis overview', 'summary': 'Covers the various applications of python, including web scraping and web development, and outlines the data lifecycle from data warehousing to visualization.', 'duration': 58.651, 'highlights': ['The chapter covers the various applications of Python, including web scraping and web development.', 'Outlines the data lifecycle starting from data warehousing to data visualization.', 'Python can be used for web scraping to extract contents from a web page.', 'Python can be used for web development.']}, {'end': 10139.927, 'start': 9931.867, 'title': 'Data analysis with python', 'summary': "Discusses the data lifecycle, including data storage, transformation, warehousing, analysis, and visualization, and then delves into the specifics of data analysis using python's pandas module for data manipulation and analysis, which is built on top of numpy, scipy, and matplotlib.", 'duration': 208.06, 'highlights': ['The chapter discusses the data lifecycle, including data storage, transformation, warehousing, analysis, and visualization. It explains the process of storing data in different formats, transforming it into a single format, data warehousing, performing analysis like predictive modeling and joining data, and data visualization.', "Python's Pandas module is used for data manipulation and analysis. Pandas is a software module written for Python programming language, enabling high-performance data manipulation and analysis.", 'Pandas is built on top of numpy, scipy, and matplotlib. Pandas is built on top of numpy, scipy, and matplotlib, which are fundamental packages for scientific computing, containing tools for linear algebra, integration, optimization, and data visualization.']}, {'end': 10769.783, 'start': 10139.967, 'title': 'Pandas data frame operations', 'summary': 'Introduces the process of creating a pandas data frame from a dictionary, demonstrating the addition of columns and the conversion of the dictionary into a data frame, followed by an overview of basic operations including slicing, merging, and joining data frames, emphasizing the practical application of each operation.', 'duration': 629.816, 'highlights': ['The chapter introduces the process of creating a Pandas data frame from a dictionary Demonstrating the addition of columns and the conversion of the dictionary into a data frame', 'An overview of basic operations including slicing, merging, and joining data frames Emphasizing the practical application of each operation']}, {'end': 11399.314, 'start': 10770.124, 'title': 'Performing data operations with pandas', 'summary': 'Demonstrates performing join operation, changing index and column headers, concatenation, and data munging using pandas, with practical examples and key points like joining data frames, changing index and column headers, and converting data formats.', 'duration': 629.19, 'highlights': ['Performed join operation to merge two data frames Demonstrated how to perform join operation in PyCharm using df1 and df2, removing key value pairs, modifying index values, and printing the joined data frames.', 'Changed index and column headers of a data frame Showed how to change the index value and column headers of a data frame, practically demonstrating the process of setting a new index and renaming column headers.', 'Executed concatenation of two data frames Illustrated the process of concatenating two data frames using the pd.concat function and printing the concatenated data frames with modified index values.', 'Practically demonstrated data munging by converting CSV to HTML Practically showed the process of reading a CSV file using the pandas module, converting it to an HTML file, and displaying the HTML table in a browser, showcasing the data munging capability of pandas.']}, {'end': 11698.365, 'start': 11400.274, 'title': 'Youth unemployment data analysis', 'summary': 'Covers a data analysis on the global youth unemployment, showing the percentage change in unemployed youth for every country from 2010 to 2011, and visualizing the findings through a bar plot, revealing specific percentage changes for countries like afghanistan, angola, albania, arab world, and united arab emirates.', 'duration': 298.091, 'highlights': ['The data analysis focuses on finding the percentage change in unemployed youth for every country from 2010 to 2011, revealing specific changes for countries like Afghanistan, Angola, Albania, Arab world, and United Arab Emirates.', 'The data set includes the percentage of unemployed youth for every country from 2010 to 2014, enabling a comprehensive analysis of the trend over the years.', 'Specific percentage changes are highlighted for countries like Afghanistan (0.25% rise), Angola (reduced percentage), Albania (1.25% increase), Arab world (3.1% increase), and United Arab Emirates (no change).', 'The analysis extends to finding percentage changes for other years, such as 2011 to 2012, providing a broader understanding of the trend over multiple years.']}], 'duration': 1825.229, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY9873136.jpg', 'highlights': ['Pandas is a software module for high-performance data manipulation and analysis.', 'The chapter covers the various applications of Python, including web scraping and web development.', 'The data lifecycle includes storage, transformation, warehousing, analysis, and visualization.', 'Demonstrates creating a Pandas data frame from a dictionary and basic operations like slicing and merging.', 'Practically demonstrated data munging by converting CSV to HTML and performing data analysis on global youth unemployment.']}, {'end': 14589.787, 'segs': [{'end': 11747.289, 'src': 'embed', 'start': 11720.217, 'weight': 3, 'content': [{'end': 11725.659, 'text': "So I've shown you four basic operations that are mean, mode, median, and variance.", 'start': 11720.217, 'duration': 5.442}, {'end': 11727.387, 'text': 'Let me explain you all of these terms.', 'start': 11726.226, 'duration': 1.161}, {'end': 11735.116, 'text': 'So what do you mean by mean? Mean is nothing but the automatic mean or the average value of a particular list or any particular sequence.', 'start': 11727.407, 'duration': 7.709}, {'end': 11739.881, 'text': 'When we talk about median, median is what? The median, the middle value.', 'start': 11735.576, 'duration': 4.305}, {'end': 11741.903, 'text': 'So they can be high median and low median.', 'start': 11739.901, 'duration': 2.002}, {'end': 11744.646, 'text': 'Then we have a sequence in which there are odd number of elements.', 'start': 11742.223, 'duration': 2.423}, {'end': 11747.289, 'text': 'So at that time, median will be the center most value.', 'start': 11744.986, 'duration': 2.303}], 'summary': 'Introduction to mean, mode, median, and variance operations in statistics.', 'duration': 27.072, 'max_score': 11720.217, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY11720217.jpg'}, {'end': 12472.514, 'src': 'embed', 'start': 12438.901, 'weight': 0, 'content': [{'end': 12445.562, 'text': 'sci-fi provides a function named quad that can calculate the integral of a function which has just one variable.', 'start': 12438.901, 'duration': 6.661}, {'end': 12449.559, 'text': 'The limits can range anywhere between plus and minus Infinity.', 'start': 12446.337, 'duration': 3.222}, {'end': 12453.422, 'text': 'So now let me jump onto my Jupiter notebook and see how this function works.', 'start': 12450.32, 'duration': 3.102}, {'end': 12455.723, 'text': "I'll just create a heading over here.", 'start': 12454.502, 'duration': 1.221}, {'end': 12472.514, 'text': "So before I make use of the quad function, I'll have to import the integrate sub module from the sci-fi library and to do this.", 'start': 12465.99, 'duration': 6.524}], 'summary': "Sci-fi library's quad function calculates integrals with one variable, allowing limits from minus to plus infinity.", 'duration': 33.613, 'max_score': 12438.901, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY12438901.jpg'}, {'end': 13307.527, 'src': 'embed', 'start': 13281.535, 'weight': 2, 'content': [{'end': 13287.078, 'text': 'So this is how you find insights in data where visualization plays a very, very important role, guys.', 'start': 13281.535, 'duration': 5.543}, {'end': 13288.939, 'text': 'So I hope you have understood this flow.', 'start': 13287.398, 'duration': 1.541}, {'end': 13293.381, 'text': "So we'll move forward and understand what exactly is matplotlib.", 'start': 13289.859, 'duration': 3.522}, {'end': 13297.943, 'text': 'Now it is very important for us to understand as to how matplotlib works fundamentally.', 'start': 13294.341, 'duration': 3.602}, {'end': 13300.415, 'text': 'Now it is pretty easy and pretty basic.', 'start': 13298.752, 'duration': 1.663}, {'end': 13305.223, 'text': 'You have some data, then your computer will draw that data to a canvas of some sort.', 'start': 13300.755, 'duration': 4.468}, {'end': 13307.527, 'text': "But it is only in the computer's memory.", 'start': 13305.523, 'duration': 2.004}], 'summary': "Data visualization is crucial for gaining insights, and matplotlib is fundamental for drawing data to a canvas in the computer's memory.", 'duration': 25.992, 'max_score': 13281.535, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY13281535.jpg'}, {'end': 14015.013, 'src': 'embed', 'start': 13987.318, 'weight': 1, 'content': [{'end': 13990.501, 'text': "Go ahead and run this again and you'll see that legend has been added.", 'start': 13987.318, 'duration': 3.183}, {'end': 13992.823, 'text': 'So here we have line one and line two as a legend.', 'start': 13990.561, 'duration': 2.262}, {'end': 13999.189, 'text': 'And we have changed the default line width, title, y-axis, x-axis labels, plus we have grid lines.', 'start': 13993.263, 'duration': 5.926}, {'end': 14002.532, 'text': 'So this is how you can customize and add style to your graph.', 'start': 13999.269, 'duration': 3.263}, {'end': 14008.937, 'text': "So what I'll do, I'll open my slides again and we are going to look at how to plot various types of graphs.", 'start': 14003.753, 'duration': 5.184}, {'end': 14011.7, 'text': 'For example, a bar graph or a histogram, all those things.', 'start': 14009.138, 'duration': 2.562}, {'end': 14015.013, 'text': "Fine, so first we'll look at how to plot a bar graph.", 'start': 14012.472, 'duration': 2.541}], 'summary': 'Demonstrates customizing graph with legend, labels, and grid lines. explains plotting bar graph.', 'duration': 27.695, 'max_score': 13987.318, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY13987318.jpg'}, {'end': 14178.092, 'src': 'embed', 'start': 14151.991, 'weight': 4, 'content': [{'end': 14156.653, 'text': 'After that we have defined one more list or a variable bins in which we have multiple numbers again.', 'start': 14151.991, 'duration': 4.662}, {'end': 14162.276, 'text': 'Now instead of using plt.bar for bar plot, for histogram we use hist, plt.hist.', 'start': 14156.933, 'duration': 5.343}, {'end': 14163.977, 'text': 'Then comes population ages.', 'start': 14162.556, 'duration': 1.421}, {'end': 14167.559, 'text': 'Instead of data, I am filling in here variables that contains data.', 'start': 14164.397, 'duration': 3.162}, {'end': 14169.802, 'text': 'then bins, then his type.', 'start': 14168.059, 'duration': 1.743}, {'end': 14171.444, 'text': 'I want it to be a bar type.', 'start': 14169.902, 'duration': 1.542}, {'end': 14173.807, 'text': 'Now when I talk about his type, I want it to be bar.', 'start': 14171.684, 'duration': 2.123}, {'end': 14175.969, 'text': 'Then the width should be 0.8.', 'start': 14174.407, 'duration': 1.562}, {'end': 14176.31, 'text': "That's all.", 'start': 14175.969, 'duration': 0.341}, {'end': 14178.092, 'text': "That's all you can understand.", 'start': 14176.89, 'duration': 1.202}], 'summary': 'Using plt.hist for histogram with bar type and width 0.8.', 'duration': 26.101, 'max_score': 14151.991, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY14151991.jpg'}], 'start': 11698.405, 'title': 'Python data analysis and visualization', 'summary': 'Covers python data analysis basics including mean, median, mode, and variance, and introduces the scipy library, its sub-packages, and special functions. it also discusses univariate interpolation, data visualization, and the fundamental working of matplotlib for visualizing data.', 'chapters': [{'end': 11888.168, 'start': 11698.405, 'title': 'Python data analysis basics', 'summary': 'Introduces python data analysis basics including mean, median, mode, and variance, and demonstrates their calculation using python, with an example sequence and its results.', 'duration': 189.763, 'highlights': ['The chapter introduces Python data analysis basics such as mean, median, mode, and variance, and demonstrates their calculation using Python, with an example sequence and its results.', 'The mean, median, mode, and variance are explained, and their calculation is demonstrated using a specific sequence, providing a clear understanding of their concepts and practical application in Python.', 'The demonstration includes importing the statistics module in Python, calculation of mean, median, mode, and variance for specific sequences, and obtaining the results, such as mean value, middle element, mode, and variation.', 'The explanation includes a demonstration of finding the mean, median, mode, and variance for specific sequences using Python, resulting in the mean value, middle element, mode, and variation.']}, {'end': 12239.612, 'start': 11888.168, 'title': 'Introduction to scipy library', 'summary': 'Introduces the scipy library, explaining its relationship with numpy, its sub-packages, and functions. it emphasizes the importance of importing sub-packages before using them and demonstrates the usage of help, info, and source functions for retrieving information about sub-packages and packages within scipy.', 'duration': 351.444, 'highlights': ['SciPy is an open-source Python library used for scientific and mathematical problem-solving, built on the NumPy extension. It is important to note that SciPy is an open-source Python library used for scientific and mathematical problem-solving, built on the NumPy extension.', 'The chapter explains the difference between NumPy and SciPy, emphasizing that SciPy contains full-featured versions of functions for scientific analysis, while NumPy provides basic operations and array data. The chapter explains the difference between NumPy and SciPy, emphasizing that SciPy contains full-featured versions of functions for scientific analysis, while NumPy provides basic operations and array data.', 'It is highlighted that SciPy has various sub-packages for scientific computations, including cluster, constants, FFT pack, integrate, interpolate, etc. It is highlighted that SciPy has various sub-packages for scientific computations, including cluster, constants, FFT pack, integrate, interpolate, etc.', 'The usage of help, info, and source functions for retrieving information about sub-packages and packages within SciPy is demonstrated, emphasizing the importance of importing sub-packages before using them. The usage of help, info, and source functions for retrieving information about sub-packages and packages within SciPy is demonstrated, emphasizing the importance of importing sub-packages before using them.']}, {'end': 12917.715, 'start': 12240.678, 'title': 'Special functions in sci-fi', 'summary': 'Discusses special functions in the sci-fi library, including exponential, trigonometric, integration, fourier transform, and linear algebra functions, demonstrating their usage and providing examples.', 'duration': 677.037, 'highlights': ['Sci-fi provides functions for computing 10 power X or 2 power X, and trigonometric functions such as sine and cosine. Sci-fi provides functions for computing 10 power X or 2 power X, and trigonometric functions such as sine and cosine.', 'The quad function in Sci-fi can calculate the integral of a function with a single variable, with limits ranging from plus to minus Infinity. The quad function in Sci-fi can calculate the integral of a function with a single variable, with limits ranging from plus to minus Infinity.', 'Sci-fi also offers the DBL quad function for solving double integration problems, as well as functions for Fourier transform and inverse Fourier transform. Sci-fi also offers the DBL quad function for solving double integration problems, as well as functions for Fourier transform and inverse Fourier transform.', 'The Sci-fi library provides functions for linear algebra, including finding the inverse of a matrix using the inv function. The Sci-fi library provides functions for linear algebra, including finding the inverse of a matrix using the inv function.', 'The interpolate sub package of Sci-fi consists of SP line functions, one-dimensional and multi-dimensional interpolation classes, etc. The interpolate sub package of Sci-fi consists of SP line functions, one-dimensional and multi-dimensional interpolation classes, etc.']}, {'end': 13281.235, 'start': 12918.536, 'title': 'Univariate interpolation and data visualization', 'summary': 'Covers the use of interp 1d function in scipy for univariate interpolation and the importance of data visualization in enabling decision makers to grasp difficult concepts or identify new patterns, with a process involving visualization, analysis, documenting insights, and transforming the dataset.', 'duration': 362.699, 'highlights': ['The chapter explains the use of interp 1D function in scipy for univariate interpolation, demonstrating the computation of X 1 and Y 1 between X and Y. The use of interp 1D function in scipy for univariate interpolation is demonstrated for computing X 1 and Y 1 between X and Y.', 'The importance of data visualization is emphasized, highlighting its role in enabling decision makers to grasp difficult concepts or identify new patterns. Data visualization is important for enabling decision makers to grasp difficult concepts or identify new patterns in the data.', 'The process of visualization, analysis, documenting insights, and transforming the dataset is outlined as a method for finding insights in the data. A process involving visualization, analysis, documenting insights, and transforming the dataset is outlined as a method for finding insights in the data.']}, {'end': 14589.787, 'start': 13281.535, 'title': 'Understanding matplotlib for data visualization', 'summary': 'Discusses the fundamental working of matplotlib to visualize data, including the types of plots available, such as bar graphs, histograms, scatter plots, pie plots, and area plots, and the customization options such as adding titles, labels, grid lines, and legends to the plots. it also covers handling multiple plots using the subplot function.', 'duration': 1308.252, 'highlights': ['The chapter discusses the fundamental working of Matplotlib to visualize data, including the types of plots available, such as bar graphs, histograms, scatter plots, pie plots, and area plots. The transcript explains the importance of understanding how Matplotlib works fundamentally and introduces various types of plots, including bar graphs, histograms, scatter plots, pie plots, hexagonal bin plots, and area plots.', 'It covers the customization options such as adding titles, labels, grid lines, and legends to the plots. The transcript provides detailed explanations on adding titles, labels, grid lines, and legends to the plots, emphasizing the importance of these elements for better interpretation and understanding of the visualized data.', 'It also covers handling multiple plots using the subplot function. The transcript introduces the subplot function for handling multiple plots, explaining its usage and demonstrating how to create multiple plots within a single figure.']}], 'duration': 2891.382, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY11698405.jpg', 'highlights': ['The chapter introduces Python data analysis basics such as mean, median, mode, and variance, and demonstrates their calculation using Python, with an example sequence and its results.', 'SciPy is an open-source Python library used for scientific and mathematical problem-solving, built on the NumPy extension.', 'The Sci-fi library provides functions for linear algebra, including finding the inverse of a matrix using the inv function.', 'The chapter explains the use of interp 1D function in scipy for univariate interpolation, demonstrating the computation of X 1 and Y 1 between X and Y.', 'The chapter discusses the fundamental working of Matplotlib to visualize data, including the types of plots available, such as bar graphs, histograms, scatter plots, pie plots, and area plots.']}, {'end': 15740.132, 'segs': [{'end': 14693.612, 'src': 'embed', 'start': 14668.587, 'weight': 0, 'content': [{'end': 14675.275, 'text': 'Before we move on, just make sure you subscribe to our channel and hit the bell icon to stay updated with all the latest Edureka videos.', 'start': 14668.587, 'duration': 6.688}, {'end': 14682.923, 'text': 'Coming back towards this session, we shall first begin with a small introduction to Seaborn and the advantages of Seaborn over Matplotlib.', 'start': 14676.135, 'duration': 6.788}, {'end': 14688.009, 'text': "Then I'll be showing you all the installation of seaborn along with its dependencies.", 'start': 14683.507, 'duration': 4.502}, {'end': 14693.612, 'text': 'following that, we shall take a look at the various plotting functions in seaborn and how you can create multi-plot grids.', 'start': 14688.009, 'duration': 5.603}], 'summary': 'Introduction to seaborn, advantages over matplotlib, installation, and various plotting functions covered.', 'duration': 25.025, 'max_score': 14668.587, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY14668587.jpg'}], 'start': 14590.247, 'title': 'Data visualization with seaborn', 'summary': 'Explains plotting functions in seaborn, advantages over matplotlib, statistical relationship visualization, categorical data plotting, and data distribution visualization in python, emphasizing ease of use and functionalities.', 'chapters': [{'end': 14849.938, 'start': 14590.247, 'title': 'Plotting functions in seaborn', 'summary': 'Explains the usage of subplot in seaborn to create multiple plots, the advantages of seaborn over matplotlib, and the installation and dependencies of seaborn, emphasizing its ease of use and functionalities.', 'duration': 259.691, 'highlights': ['Seaborn allows the creation of multiple plots using the subplot function, with examples of aligning plots vertically and horizontally. Examples of using subplot two, one, one and subplot two, one, two to create multiple plots.', 'Seaborn offers advantages over Matplotlib, such as ease of use for complex visualizations, support for multi-plot grids, and availability of different color palettes. Seaborn eases building complex visualizations, supports multi-plot grids, and provides different color palettes.', "Seaborn's installation requires simple commands like 'pip install seaborn' or 'conda install seaborn' and mandatory dependencies including numpy, scipy, matplotlib, and pandas. Installation commands 'pip install seaborn' or 'conda install seaborn' and mandatory dependencies."]}, {'end': 15252.228, 'start': 14849.938, 'title': 'Seaborn data visualization', 'summary': 'Introduces the use of seaborn for visualizing statistical relationships and plotting categorical data using relplot and cat plot functions, with examples demonstrating scatter plots, line plots, and different plot types like strip plot, swarm plot, box plot, and violin plot.', 'duration': 402.29, 'highlights': ['Seaborn provides functions for visualizing statistical relationships and categorical data. Seaborn offers functions for visualizing statistical relationships, plotting with categorical data, and visualizing the distribution of a data set.', 'Introduction to relplot function for visualizing statistical relationships using scatter and line plots. The relplot function is used to plot various statistical relationships and makes use of scatter plot and line plot as access level functions.', 'Demonstration of loading and visualizing a data set using relplot function in Jupyter notebook. An example is provided on how to load and visualize a data set using the relplot function in a Jupyter notebook.', 'Explanation of using the hue semantic to add another dimension to the scatter plot. The use of the hue semantic is explained to add another dimension to the scatter plot created using the relplot function.', 'Introduction to cat plot function for plotting categorical data with scatter, distribution, and estimate plots. The cat plot function is introduced for plotting categorical data and is characterized by three families of access level functions: scatter plots, distribution plots, and estimate plots.', 'Demonstration of plotting categorical data using the cat plot function in Jupyter notebook. An example is given to demonstrate how to plot categorical data using the cat plot function in a Jupyter notebook.', 'Illustration of changing the plot type using the kind parameter in cat plot function. The process of changing the plot type, such as scatter plot, violin plot, and box plot, using the kind parameter in the cat plot function is demonstrated.']}, {'end': 15740.132, 'start': 15252.968, 'title': 'Visualizing data distributions', 'summary': 'Explains how to visualize univariate and bivariate distributions using seaborn in python, demonstrating functions such as dist plot, joint plot, facet grid, pair grid, set function, and despine function, showcasing their applications and impact on the visualization of data.', 'duration': 487.164, 'highlights': ['Seaborn also provides a number of color palettes and to check the color palettes that are available in Seaborn. Seaborn provides a variety of color palettes, and the available options can be checked using the color palette function.', 'You can change the style parameter to any of the available themes, which is dark grid, dark white, etc. The style parameter can be modified to select from available themes such as dark grid, dark white, etc., altering the appearance of the plots.', 'Seaborn also allows you to plot multiple grids side-by-side using the facet grid function or the pair grid function. Seaborn enables the plotting of multiple grids side-by-side using functions like facet grid or pair grid, facilitating visual comparison of multiple plots.']}], 'duration': 1149.885, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY14590247.jpg', 'highlights': ['Seaborn offers advantages over Matplotlib, such as ease of use for complex visualizations, support for multi-plot grids, and availability of different color palettes.', 'Seaborn provides functions for visualizing statistical relationships and categorical data.', 'Seaborn allows the creation of multiple plots using the subplot function, with examples of aligning plots vertically and horizontally.', 'Introduction to relplot function for visualizing statistical relationships using scatter and line plots.', 'Seaborn also provides a number of color palettes and to check the color palettes that are available in Seaborn.', "Seaborn's installation requires simple commands like 'pip install seaborn' or 'conda install seaborn' and mandatory dependencies including numpy, scipy, matplotlib, and pandas.", 'Seaborn also allows you to plot multiple grids side-by-side using the facet grid function or the pair grid function.']}, {'end': 18049.734, 'segs': [{'end': 15990.512, 'src': 'embed', 'start': 15961.947, 'weight': 5, 'content': [{'end': 15965.832, 'text': "All right, so let's move forward and we'll focus on few applications of machine learning.", 'start': 15961.947, 'duration': 3.885}, {'end': 15972.116, 'text': 'Guys, applications of machine learning, you can find it anywhere.', 'start': 15968.513, 'duration': 3.603}, {'end': 15977.06, 'text': "There are like wide varieties of applications and you look around, you're surrounded with applications of machine learning.", 'start': 15972.396, 'duration': 4.664}, {'end': 15979.823, 'text': "So I'll just discuss three of those applications.", 'start': 15977.541, 'duration': 2.282}, {'end': 15990.512, 'text': 'So Siri, all you rich folks, all iPhone users, they know what Siri is and probably a lot of other people also know what Siri is.', 'start': 15980.303, 'duration': 10.209}], 'summary': 'Discussion on three machine learning applications, including siri for iphone users.', 'duration': 28.565, 'max_score': 15961.947, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY15961947.jpg'}, {'end': 16483.543, 'src': 'embed', 'start': 16456.973, 'weight': 11, 'content': [{'end': 16464.699, 'text': 'We move ahead to multivariate calculus where we will learn about differentiation partial derivatives and how they can help us in reality.', 'start': 16456.973, 'duration': 7.726}, {'end': 16468.322, 'text': 'So I hope you are clear with the topics being covered for today.', 'start': 16465.58, 'duration': 2.742}, {'end': 16476.507, 'text': 'Now before we get started subscribe to the edureka YouTube channel and hit the bell icon to never miss an update from us on the trending Technologies.', 'start': 16469.081, 'duration': 7.426}, {'end': 16483.543, 'text': "Also, if you're looking for an online training certification on machine learning check out the link in the description box below.", 'start': 16477.293, 'duration': 6.25}], 'summary': 'Introduction to multivariate calculus and a call to action for subscribing and exploring online training on machine learning.', 'duration': 26.57, 'max_score': 16456.973, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY16456973.jpg'}, {'end': 16639.629, 'src': 'embed', 'start': 16609.2, 'weight': 0, 'content': [{'end': 16611.361, 'text': 'a scalar is basically a value.', 'start': 16609.2, 'duration': 2.161}, {'end': 16613.401, 'text': 'you, it represents something, right?', 'start': 16611.361, 'duration': 2.04}, {'end': 16617.142, 'text': 'So scalars are just values that represent something.', 'start': 16613.861, 'duration': 3.281}, {'end': 16621.743, 'text': 'suppose we had a laptop on sale and it is priced at 50,000 rupees, right?', 'start': 16617.142, 'duration': 4.601}, {'end': 16625.965, 'text': 'So this 50,000 rupees is the scalar value of that laptop.', 'start': 16622.063, 'duration': 3.902}, {'end': 16629.766, 'text': 'what are the operations that can be performed on scalars?', 'start': 16626.564, 'duration': 3.202}, {'end': 16632.807, 'text': 'first, it is just basic arithmetic.', 'start': 16629.766, 'duration': 3.041}, {'end': 16636.828, 'text': 'so, for example, we have addition, subtraction, multiplication, division.', 'start': 16632.807, 'duration': 4.021}, {'end': 16639.629, 'text': 'all of those operations can be applied on scalars.', 'start': 16636.828, 'duration': 2.801}], 'summary': 'Scalars are values representing something. basic arithmetic operations can be performed on scalars.', 'duration': 30.429, 'max_score': 16609.2, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY16609200.jpg'}, {'end': 17174.884, 'src': 'embed', 'start': 17145.148, 'weight': 4, 'content': [{'end': 17148.89, 'text': 'you need to understand what a function is trying to convey to you.', 'start': 17145.148, 'duration': 3.742}, {'end': 17152.372, 'text': "let's say, for example, you have the equation of a straight line.", 'start': 17148.89, 'duration': 3.482}, {'end': 17154.618, 'text': 'So what does the straight line be?', 'start': 17153.038, 'duration': 1.58}, {'end': 17157.459, 'text': 'it is y is equal to MX plus C.', 'start': 17154.618, 'duration': 2.841}, {'end': 17164.882, 'text': 'What does this mean? It means that the y coordinate is equal to some value M into X plus a constant.', 'start': 17157.459, 'duration': 7.423}, {'end': 17170.963, 'text': "So I'm able to plot this I'll be getting the y coordinate the x coordinate and I'll be able to get a straight line.", 'start': 17165.762, 'duration': 5.201}, {'end': 17174.884, 'text': 'So whatever numbers I put into these I will always be getting a straight line.', 'start': 17170.983, 'duration': 3.901}], 'summary': 'Understanding the equation y=mx+c helps in plotting a straight line.', 'duration': 29.736, 'max_score': 17145.148, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY17145148.jpg'}, {'end': 17568.808, 'src': 'embed', 'start': 17545.054, 'weight': 1, 'content': [{'end': 17554.059, 'text': "So all that you've learned by now is basically adding values of vectors, subtracting vectors, multiplying two vectors, then transpose that is,", 'start': 17545.054, 'duration': 9.005}, {'end': 17557.822, 'text': "the flipping of vectors, and now you're going to learn about determinant.", 'start': 17554.059, 'duration': 3.763}, {'end': 17562.464, 'text': 'What is a determinant? All the matrices that we had till now.', 'start': 17558.522, 'duration': 3.942}, {'end': 17564.325, 'text': 'They are basically directions.', 'start': 17562.784, 'duration': 1.541}, {'end': 17568.808, 'text': 'They are basically directions and values of all the vectors.', 'start': 17565.006, 'duration': 3.802}], 'summary': 'Learned vector operations and now will learn about determinants in matrices.', 'duration': 23.754, 'max_score': 17545.054, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY17545054.jpg'}, {'end': 17850.833, 'src': 'embed', 'start': 17824.753, 'weight': 2, 'content': [{'end': 17829.276, 'text': "right?. I'm assuming that there is going to be a 2 cross 2 matrix.", 'start': 17824.753, 'duration': 4.523}, {'end': 17832.679, 'text': "whenever you're scaling, it is basically increasing the size.", 'start': 17829.276, 'duration': 3.403}, {'end': 17834.1, 'text': 'So how do you increase the size?', 'start': 17832.759, 'duration': 1.341}, {'end': 17841.326, 'text': 'It is SX and SY which are the scaling factors which you perform on your x and y coordinates, and you have sharing,', 'start': 17834.68, 'duration': 6.646}, {'end': 17846.169, 'text': "which is basically moving or reshaping your particular object that you're working with.", 'start': 17841.326, 'duration': 4.843}, {'end': 17850.833, 'text': 'so it can be M, which is the sharing factor, and then you have the rotation.', 'start': 17846.169, 'duration': 4.664}], 'summary': 'Discussing scaling, sharing, and rotation in 2d matrices.', 'duration': 26.08, 'max_score': 17824.753, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY17824753.jpg'}], 'start': 15744.115, 'title': 'Mathematics in machine learning', 'summary': 'Covers the importance of mathematics in machine learning, emphasizing key topics such as linear algebra, multivariate calculus, and the essential math required for mastering machine learning with a focus on real-life application. it also delves into the significance of linear algebra in machine learning and provides an introductory explanation of scalars and their operations.', 'chapters': [{'end': 16412.877, 'start': 15744.115, 'title': 'Introduction to machine learning', 'summary': 'Introduces the concept of machine learning, its applications in e-commerce, healthcare, and marketing, and explains the types of machine learning including supervised, unsupervised, and reinforcement learning.', 'duration': 668.762, 'highlights': ['Machine learning is a subset of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. Machine learning allows computers to learn without explicit programming, enabling them to detect patterns and create models from training data.', 'E-commerce websites like Amazon and Flipkart use machine learning to recommend products based on user data, leading to increased sales and better insights. E-commerce websites utilize machine learning to recommend products based on user data, resulting in improved sales and enhanced customer experience.', 'Applications of machine learning are found in healthcare, allowing for the analysis of health history to identify individuals at risk of specific diseases, enabling preventive measures. Machine learning in healthcare analyzes health history to identify individuals at risk of diseases, facilitating preventive measures and personalized care.', 'Supervised learning involves training a model using labeled data to learn the mapping function between input and output variables, enabling prediction based on new input data. Supervised learning trains a model using labeled data to understand the relationship between input and output, facilitating accurate predictions based on new input data.', 'Unsupervised learning involves training a model with unclassified and unlabeled data, creating clusters based on similarities and dissimilarities, such as K-means clustering. Unsupervised learning creates clusters from unclassified data based on similarities and dissimilarities, with K-means clustering being a notable algorithm.', 'Reinforcement learning enables machines to learn from experience, similar to human learning, by receiving rewards for desired actions and penalties for undesired ones. Reinforcement learning allows machines to learn from experience by receiving rewards for desired actions and penalties for undesired ones, akin to human learning.']}, {'end': 16678.628, 'start': 16413.978, 'title': 'Mathematics in machine learning', 'summary': 'Covers the importance of mathematics in machine learning, emphasizing the key topics of linear algebra, multivariate calculus, and the essential math required for mastering machine learning, with a focus on the application of mathematics in real-life scenarios and the pie chart depicting the distribution of various math domains. it also delves into the significance of linear algebra in machine learning and provides an introductory explanation of scalars and their operations.', 'duration': 264.65, 'highlights': ['The chapter covers the importance of mathematics in machine learning, emphasizing the key topics of linear algebra, multivariate calculus, and the essential math required for mastering machine learning, with a focus on the application of mathematics in real-life scenarios and the pie chart depicting the distribution of various math domains.', 'Linear algebra is used most widely in machine learning, covering various aspects and making it unavoidable for learning mathematics for machine learning. It helps in optimizing data operations and can be performed on pixels, such as sharing a rotation, and much more, demonstrating its importance in mathematics for machine learning.', 'A pie chart is presented which illustrates the distribution of various math domains required for mastering machine learning, with linear algebra covering a major part, followed by multivariate calculus, and statistics and probability playing a significant role, providing a visual representation of the essential math domains in machine learning.', 'An introductory explanation of scalars is provided, defining them as values that represent something and explaining the operations that can be performed on scalars, such as basic arithmetic operations like addition, subtraction, multiplication, and division, with illustrative examples of buying a laptop and accessories and applying a 50% discount, showcasing their practical application in real-life scenarios.']}, {'end': 17002.123, 'start': 16678.808, 'title': 'Understanding vectors & operations', 'summary': 'Explains the various interpretations of vectors by computer scientists, physicists, and mathematicians, emphasizing their importance in machine learning and covers vector addition, scalar multiplication, and projection with practical examples.', 'duration': 323.315, 'highlights': ['The chapter explains the various interpretations of vectors by computer scientists, physicists, and mathematicians, emphasizing their importance in machine learning. It discusses how vectors are interpreted by different disciplines and highlights their significance in machine learning.', 'It covers vector addition, scalar multiplication, and projection with practical examples. The chapter provides practical examples of vector addition, scalar multiplication, and projection, demonstrating their applications and implications.', 'Vector addition is explained as the total work done by both vectors in a quantified form, with an example of walking forward and then moving right. It clarifies vector addition as the quantified total work done by two vectors, illustrated by an example of walking forward and moving right.', 'Scalar multiplication is described as the process of a vector growing or shrinking when multiplied by a scalar value, demonstrated with positive and negative scalar values. It details scalar multiplication as the process of a vector growing or shrinking when multiplied by positive or negative scalar values.', 'The concept of projection is introduced as a method to obtain information about an unknown vector by projecting it onto a known vector, with applications in deep learning. It introduces projection as a method to obtain information about an unknown vector by projecting it onto a known vector, showcasing its relevance in deep learning.']}, {'end': 17544.4, 'start': 17002.263, 'title': 'Matrices and vector operations', 'summary': 'Explains the concept of matrices, their use in converting equations, and key operations including addition, subtraction, multiplication, and transpose, crucial for machine learning applications.', 'duration': 542.137, 'highlights': ['Matrices are used to convert equations into arrays, making it easier to perform operations, and are crucial for machine learning applications. Matrices are used to convert equations into arrays, simplifying operations and are crucial for machine learning applications.', 'Understanding the concept of a function and its representation as an equation, such as that of a straight line, is crucial for interpreting and utilizing matrices in machine learning applications. Understanding the concept of a function and its representation as an equation, such as that of a straight line, is crucial for interpreting and utilizing matrices in machine learning applications.', 'Explanation of key matrix operations including addition, subtraction, multiplication, and transpose, essential for manipulating data in machine learning applications. Explanation of key matrix operations including addition, subtraction, multiplication, and transpose, essential for manipulating data in machine learning applications.']}, {'end': 18049.734, 'start': 17545.054, 'title': 'Matrix operations and applications', 'summary': 'Covers operations and applications of matrices including determinant, inverse, eigenvalues, eigenvectors, and solving equations using row echelon method, highlighting the importance of matrices in machine learning and providing tangible examples.', 'duration': 504.68, 'highlights': ['Matrices are used to understand the direction and values of vectors, with determinants providing insight into the weight and sensitivity of the data set.', 'The determinant of a matrix is crucial in obtaining eigenvalues and understanding eigenvectors, playing a significant role in machine learning.', 'The inverse of a matrix is explained using a real-world analogy and its importance in achieving zero work done and understanding vector movements is emphasized.', 'Methods for finding the inverse of a matrix are outlined, including the process for 2x2 matrices and orders 3 and above, providing a comprehensive understanding of matrix inversion.', 'The significance of using vectors as matrices in machine learning and computer graphics is highlighted, showcasing how matrices simplify operations such as scaling, rotation, and sharing on data and images.']}], 'duration': 2305.619, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY15744115.jpg', 'highlights': ['Machine learning enables computers to learn without explicit programming, leading to improved sales and personalized care.', 'Supervised learning trains a model using labeled data to facilitate accurate predictions based on new input data.', 'Unsupervised learning creates clusters from unclassified data based on similarities and dissimilarities, with K-means clustering being a notable algorithm.', 'Reinforcement learning allows machines to learn from experience by receiving rewards for desired actions and penalties for undesired ones.', 'Linear algebra is crucial for mastering machine learning, covering various aspects and optimizing data operations.', 'A pie chart illustrates the distribution of various math domains required for mastering machine learning, emphasizing the importance of linear algebra.', 'Scalars represent values and can undergo basic arithmetic operations, showcasing their practical application in real-life scenarios.', 'Vectors are interpreted by different disciplines and are crucial in machine learning, with practical examples of vector addition, scalar multiplication, and projection.', 'Matrices are crucial for machine learning applications, simplifying operations and providing insight into the weight and sensitivity of the data set.', 'Understanding the concept of a function and its representation as an equation is crucial for interpreting and utilizing matrices in machine learning applications.', 'The determinant of a matrix is crucial in obtaining eigenvalues and understanding eigenvectors, playing a significant role in machine learning.', 'The inverse of a matrix is important in achieving zero work done and understanding vector movements, with outlined methods for finding the inverse of a matrix.', 'Using vectors as matrices in machine learning and computer graphics simplifies operations such as scaling, rotation, and sharing on data and images.']}, {'end': 19951.685, 'segs': [{'end': 18616.777, 'src': 'embed', 'start': 18588.274, 'weight': 10, 'content': [{'end': 18593.779, 'text': 'So it is a very important aspect of machine learning once we are done with all of this.', 'start': 18588.274, 'duration': 5.505}, {'end': 18596, 'text': "Let's start our coding for PCA.", 'start': 18593.919, 'duration': 2.081}, {'end': 18600.934, 'text': 'We will now be coding for principal component analysis.', 'start': 18597.233, 'duration': 3.701}, {'end': 18603.534, 'text': 'Let me show you how it really works.', 'start': 18601.034, 'duration': 2.5}, {'end': 18609.055, 'text': 'So I am having all the programs already prepared for you guys.', 'start': 18604.494, 'duration': 4.561}, {'end': 18612.876, 'text': 'So we do not extend this big tutorial even more further.', 'start': 18609.135, 'duration': 3.741}, {'end': 18616.777, 'text': 'Let me go to the presentation mode mode.', 'start': 18614.196, 'duration': 2.581}], 'summary': 'Introducing pca coding with prepared programs for efficient learning.', 'duration': 28.503, 'max_score': 18588.274, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY18588274.jpg'}, {'end': 18891.318, 'src': 'embed', 'start': 18860.461, 'weight': 2, 'content': [{'end': 18861.282, 'text': "It's so easy.", 'start': 18860.461, 'duration': 0.821}, {'end': 18867.946, 'text': "Think if you're working with tons and tons of data, it becomes really really difficult when you're working with it.", 'start': 18861.782, 'duration': 6.164}, {'end': 18869.387, 'text': "That's the reason.", 'start': 18868.447, 'duration': 0.94}, {'end': 18872.357, 'text': "use PCA, and it's just not use PCA.", 'start': 18869.387, 'duration': 2.97}, {'end': 18875.961, 'text': 'make sure that you are able to reduce the data as much as you can,', 'start': 18872.357, 'duration': 3.604}, {'end': 18880.546, 'text': 'so that you can get at least all the amount of information that you can from that particular data.', 'start': 18875.961, 'duration': 4.585}, {'end': 18884.551, 'text': 'You do not need all the data, but you need the information from that data.', 'start': 18881.087, 'duration': 3.464}, {'end': 18887.594, 'text': 'That is basically how PCA works.', 'start': 18885.191, 'duration': 2.403}, {'end': 18891.318, 'text': 'I hope it was clear to you guys.', 'start': 18889.836, 'duration': 1.482}], 'summary': 'Pca helps reduce data to retain essential information.', 'duration': 30.857, 'max_score': 18860.461, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY18860461.jpg'}, {'end': 19009.59, 'src': 'embed', 'start': 18964.361, 'weight': 0, 'content': [{'end': 18967.803, 'text': 'so, for example, right here i have y is equal to e power x.', 'start': 18964.361, 'duration': 3.442}, {'end': 18969.965, 'text': 'it is really easy to tell that.', 'start': 18967.803, 'duration': 2.162}, {'end': 18973.167, 'text': 'okay, if it is e power x, this is going to be the graph for it.', 'start': 18969.965, 'duration': 3.202}, {'end': 18975.949, 'text': 'but look at, y is equal to 1 by x.', 'start': 18973.167, 'duration': 2.782}, {'end': 18981.893, 'text': 'it is such a horrendous graph that i cannot explain it using a particular equation.', 'start': 18975.949, 'duration': 5.944}, {'end': 18983.854, 'text': 'those are the types of equations.', 'start': 18981.893, 'duration': 1.961}, {'end': 18986.136, 'text': 'those are the good functions and the bad functions.', 'start': 18983.854, 'duration': 2.282}, {'end': 18988.212, 'text': 'We know all of this part.', 'start': 18987.031, 'duration': 1.181}, {'end': 18992.838, 'text': "What are we really after? So let's do all of that right now.", 'start': 18988.493, 'duration': 4.345}, {'end': 19000.787, 'text': "Okay So most of us already know all of this but what are we really after? So let's understand that with a really simple example.", 'start': 18993.639, 'duration': 7.148}, {'end': 19006.174, 'text': "So let's assume that we have a car moving in a single direction only and is already in motion.", 'start': 19001.368, 'duration': 4.806}, {'end': 19009.59, 'text': 'So if we plot a graph of its speed versus time,', 'start': 19006.707, 'duration': 2.883}], 'summary': 'Contrasting exponential and reciprocal functions, with a focus on understanding their graphs and practical applications.', 'duration': 45.229, 'max_score': 18964.361, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY18964361.jpg'}, {'end': 19825.634, 'src': 'embed', 'start': 19776.792, 'weight': 8, 'content': [{'end': 19782.053, 'text': "and then I'm going to find all the gradients and then I'm going to pass the gradient into it and then update the weight.", 'start': 19776.792, 'duration': 5.261}, {'end': 19789.976, 'text': 'Let me show you an example now how increasing the number of steps is going to make me reach my Target much more better.', 'start': 19783.034, 'duration': 6.942}, {'end': 19800.996, 'text': 'So as you can see my initial weight was a 0.50 thing and all that and I have just run this for 10 times, right? Let me change this to thousand.', 'start': 19790.716, 'duration': 10.28}, {'end': 19804.738, 'text': 'My input is 0.1 and this is 0.3.', 'start': 19801.876, 'duration': 2.862}, {'end': 19809.162, 'text': 'I have to make 0.1 as 0.3, right? So let me run this again.', 'start': 19804.738, 'duration': 4.424}, {'end': 19812.244, 'text': 'It is still at 0.50.', 'start': 19809.182, 'duration': 3.062}, {'end': 19814.986, 'text': 'Let me add another 0 which is 10,000 times.', 'start': 19812.244, 'duration': 2.742}, {'end': 19816.347, 'text': 'Let me run this.', 'start': 19815.666, 'duration': 0.681}, {'end': 19823.392, 'text': 'So as you can see it has now become 0.47, which is much more better than what we were actually doing.', 'start': 19817.448, 'duration': 5.944}, {'end': 19825.634, 'text': 'Let me add another 0 over here.', 'start': 19823.973, 'duration': 1.661}], 'summary': 'Increasing the number of steps from 10 to 10,000 improved the weight from 0.50 to 0.47.', 'duration': 48.842, 'max_score': 19776.792, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY19776792.jpg'}, {'end': 19951.685, 'src': 'embed', 'start': 19900.114, 'weight': 3, 'content': [{'end': 19911.641, 'text': 'This is the final output that we have achieved 0.3 0 0 0 5 7 7 and it is much much much better than what we were already getting from the output.', 'start': 19900.114, 'duration': 11.527}, {'end': 19914.285, 'text': 'So this is how the gradient descent works.', 'start': 19912.423, 'duration': 1.862}, {'end': 19916.646, 'text': 'It uses differentiation.', 'start': 19914.685, 'duration': 1.961}, {'end': 19920.85, 'text': 'Where is the derivation over here? This is my derivation function.', 'start': 19916.987, 'duration': 3.863}, {'end': 19923.032, 'text': 'This is my derivation function.', 'start': 19921.47, 'duration': 1.562}, {'end': 19929.437, 'text': 'So these derivation functions are what are helping me to get my new error,', 'start': 19923.172, 'duration': 6.265}, {'end': 19941.839, 'text': 'get my new error and then put that into the gradient descent function and then basically find and make my weight much more better so that I can get the output which is from my input 0.1..', 'start': 19929.437, 'duration': 12.402}, {'end': 19944.661, 'text': 'I have to get the target of 0.3.', 'start': 19941.839, 'duration': 2.822}, {'end': 19947.282, 'text': "So I hope you've understood how the gradient descent works.", 'start': 19944.661, 'duration': 2.621}, {'end': 19951.685, 'text': 'It keeps going in and in and it uses a differentiation Lord.', 'start': 19947.542, 'duration': 4.143}], 'summary': 'Achieved 0.3 output, improved from previous results using gradient descent and differentiation.', 'duration': 51.571, 'max_score': 19900.114, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY19900114.jpg'}], 'start': 18050.314, 'title': 'Linear algebra and differentiation in machine learning', 'summary': 'Covers row echelon method, eigenvectors, pca, differentiation basics, partial differentiation, multivariate calculus, and gradient descent in machine learning, emphasizing their roles and providing a step-by-step process for solving matrices and optimizing target prediction, achieving a significant improvement to a final output of 0.30057 from the initial output of 0.50.', 'chapters': [{'end': 18525.213, 'start': 18050.314, 'title': 'Row echelon method and eigenvectors', 'summary': 'Explains the row echelon method for solving matrices, showcasing the step-by-step process to solve a system of equations, and then delves into the importance of eigenvectors in data analysis, emphasizing their role in preserving data integrity during transformations.', 'duration': 474.899, 'highlights': ['The chapter explains the row echelon method for solving matrices and showcases the step-by-step process to solve a system of equations. It demonstrates the step-by-step process of applying row operations to transform a matrix into row-echelon form, leading to the solution of a system of equations.', 'It delves into the importance of eigenvectors in data analysis, emphasizing their role in preserving data integrity during transformations. Eigenvectors are highlighted as data components that maintain their direction even after transformations, making them crucial for preserving data integrity and aiding in analysis processes.']}, {'end': 18963.741, 'start': 18525.213, 'title': 'Understanding eigenvectors and pca in machine learning', 'summary': 'Explains the importance of eigenvectors and eigenvalues in understanding linear algebra, the applications of linear algebra in machine learning including principal component analysis (pca) for dimensionality reduction, and the coding process for pca using principal component analysis to transform and reduce the dimensionality of data.', 'duration': 438.528, 'highlights': ['Eigenvectors are important in understanding linear algebra, while eigenvalues represent the values performed on these eigenvectors. Eigenvectors play a crucial role in understanding linear algebra, while eigenvalues represent the values performed on these eigenvectors.', 'The applications of linear algebra in machine learning include principal component analysis (PCA) for dimensionality reduction, single value decomposition, natural language processing, and optimization of deep learning models. Linear algebra has various applications in machine learning, including principal component analysis (PCA) for dimensionality reduction, single value decomposition, natural language processing, and optimization of deep learning models.', 'The coding process for PCA involves importing necessary libraries, generating and transforming data, fitting the data into PCA, and performing scatter plots and vector calculations to understand the components and variance of the data. The coding process for PCA involves importing necessary libraries, generating and transforming data, fitting the data into PCA, and performing scatter plots and vector calculations to understand the components and variance of the data.', 'Multivariate calculus helps optimize and increase the performance of machine learning models by solving the second most important problem in model development. Multivariate calculus plays a crucial role in optimizing and increasing the performance of machine learning models by solving the second most important problem in model development.', 'Calculus involves differentiation, which is essential for understanding the sensitivity of a function to varying inputs and improving model performance. Calculus involves differentiation, which is essential for understanding the sensitivity of a function to varying inputs and improving model performance.']}, {'end': 19422.43, 'start': 18964.361, 'title': 'Understanding differentiation basics', 'summary': 'Introduces the concept of differentiation as the rate of change between two points and the rules governing it, emphasizing its importance in machine learning optimization and presenting the basics of partial differentiation.', 'duration': 458.069, 'highlights': ['The chapter introduces the concept of differentiation as the rate of change between two points It explains how acceleration is the derivative of speed and how the rate of change between two points can be found using the derivation formula.', 'The chapter presents the basics of differentiation rules, including power rule, sum rule, product rule, and chain rule It provides detailed examples and explanations of the power rule, sum rule, product rule, and chain rule, demonstrating their application in solving equations.', 'The chapter emphasizes the importance of differentiation in machine learning optimization It highlights the crucial role of differentiation in optimizing machine learning models, showcasing its relevance in the field of technology and data science.', 'The chapter introduces the basics of partial differentiation and its significance It discusses how partial differentiation is often overlooked but holds importance, raising the question of how it has helped us achieve certain goals.']}, {'end': 19687.989, 'start': 19422.99, 'title': 'Partial differentiation and multivariate calculus', 'summary': "Explains partial differentiation using a car design example, highlighting the concept of changing one variable while keeping others constant, and its applications in multivariate calculus, including the jacobian vector and hessian's role in deep learning models.", 'duration': 264.999, 'highlights': ['Partial Differentiation in Car Design Example Illustrates the concept of changing one variable while keeping others constant in car design, emphasizing the importance of partial differentiation in maximizing performance.', 'Applications of Multivariate Calculus Explains the applications of multivariate calculus, including the use of the Jacobian vector in finding global maximum of data sets and its role in linearizing linear functions, as well as the significance of the Hessian in minimizing errors and its application in deep learning models and gradient descent for optimizing weights.']}, {'end': 19951.685, 'start': 19689.153, 'title': 'Gradient descent and weight optimization', 'summary': 'Explains the process of gradient descent and weight optimization using differentiation and derivation functions, with an example showing how increasing the number of steps significantly improves the target prediction, achieving a final output of 0.30057, much better than the initial output of 0.50, demonstrating the effectiveness of the process.', 'duration': 262.532, 'highlights': ['The example demonstrates how increasing the number of steps in gradient descent significantly improves the target prediction, achieving a final output of 0.30057, much better than the initial output of 0.50, showing the effectiveness of the process.', 'The process involves differentiation and derivation functions to update the weight and find the new error, contributing to the significant improvement in target prediction.', 'The function uses a learning rate of 0.01 and updates the weight by passing the gradient into it, demonstrating the iterative approach of gradient descent in weight optimization.', 'The initial input of 0.1 and target of 0.3 are utilized in the example to showcase the iterative process of finding the best weight to improve the output prediction.']}], 'duration': 1901.371, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY18050314.jpg', 'highlights': ['Covers row echelon method, eigenvectors, pca, differentiation basics, partial differentiation, multivariate calculus, and gradient descent in machine learning, emphasizing their roles and providing a step-by-step process for solving matrices and optimizing target prediction, achieving a significant improvement to a final output of 0.30057 from the initial output of 0.50.', 'Eigenvectors are important in understanding linear algebra, while eigenvalues represent the values performed on these eigenvectors.', 'The coding process for PCA involves importing necessary libraries, generating and transforming data, fitting the data into PCA, and performing scatter plots and vector calculations to understand the components and variance of the data.', 'Multivariate calculus helps optimize and increase the performance of machine learning models by solving the second most important problem in model development.', 'Calculus involves differentiation, which is essential for understanding the sensitivity of a function to varying inputs and improving model performance.', 'The chapter presents the basics of differentiation rules, including power rule, sum rule, product rule, and chain rule It provides detailed examples and explanations of the power rule, sum rule, product rule, and chain rule, demonstrating their application in solving equations.', 'The chapter introduces the concept of differentiation as the rate of change between two points It explains how acceleration is the derivative of speed and how the rate of change between two points can be found using the derivation formula.', 'The chapter emphasizes the importance of differentiation in machine learning optimization It highlights the crucial role of differentiation in optimizing machine learning models, showcasing its relevance in the field of technology and data science.', 'Partial Differentiation in Car Design Example Illustrates the concept of changing one variable while keeping others constant in car design, emphasizing the importance of partial differentiation in maximizing performance.', 'Applications of Multivariate Calculus Explains the applications of multivariate calculus, including the use of the Jacobian vector in finding global maximum of data sets and its role in linearizing linear functions, as well as the significance of the Hessian in minimizing errors and its application in deep learning models and gradient descent for optimizing weights.', 'The example demonstrates how increasing the number of steps in gradient descent significantly improves the target prediction, achieving a final output of 0.30057, much better than the initial output of 0.50, showing the effectiveness of the process.', 'The process involves differentiation and derivation functions to update the weight and find the new error, contributing to the significant improvement in target prediction.', 'The function uses a learning rate of 0.01 and updates the weight by passing the gradient into it, demonstrating the iterative approach of gradient descent in weight optimization.', 'The initial input of 0.1 and target of 0.3 are utilized in the example to showcase the iterative process of finding the best weight to improve the output prediction.']}, {'end': 21967.026, 'segs': [{'end': 20404.083, 'src': 'embed', 'start': 20376.2, 'weight': 13, 'content': [{'end': 20382.885, 'text': 'So for example, I feed data to my computer, right? And my data then applies clustering algorithm onto that.', 'start': 20376.2, 'duration': 6.685}, {'end': 20384.647, 'text': "So this is the kind of output that I'll get.", 'start': 20382.905, 'duration': 1.742}, {'end': 20389.431, 'text': "So it'll categorize it under group A, group B, and group C.", 'start': 20385.407, 'duration': 4.024}, {'end': 20395.616, 'text': "And then I can make a decision whether what I want to do with this data that I've got.", 'start': 20389.431, 'duration': 6.185}, {'end': 20399.079, 'text': "This computer doesn't understand anything, what this data is all about.", 'start': 20395.656, 'duration': 3.423}, {'end': 20404.083, 'text': "It doesn't understand, maybe it's of cars, maybe it's of food, maybe it's of money.", 'start': 20399.139, 'duration': 4.944}], 'summary': 'Data is clustered into groups a, b, and c to aid decision-making.', 'duration': 27.883, 'max_score': 20376.2, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY20376200.jpg'}, {'end': 20450.799, 'src': 'embed', 'start': 20422.094, 'weight': 5, 'content': [{'end': 20424.836, 'text': 'But this is what a clustering algorithm will give you.', 'start': 20422.094, 'duration': 2.742}, {'end': 20431.345, 'text': "Having said that, let's move on to the next algorithm now, which is reinforcement algorithm.", 'start': 20426.621, 'duration': 4.724}, {'end': 20436.789, 'text': 'Alright, so we discussed reinforcement learning, so that is what reinforcement algorithms are all about.', 'start': 20431.905, 'duration': 4.884}, {'end': 20440.632, 'text': 'Whenever you have to make a decision right,', 'start': 20437.169, 'duration': 3.463}, {'end': 20450.799, 'text': 'and so whenever you have to make a decision and your decision is based on the past experiences of your machine or whatever inputs that you have given to your machine,', 'start': 20440.632, 'duration': 10.167}], 'summary': 'The transcript discusses clustering and reinforcement algorithms in machine learning.', 'duration': 28.705, 'max_score': 20422.094, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY20422094.jpg'}, {'end': 20680.85, 'src': 'embed', 'start': 20640.518, 'weight': 10, 'content': [{'end': 20642.219, 'text': 'Now talking about binary classification.', 'start': 20640.518, 'duration': 1.701}, {'end': 20644.941, 'text': 'It is a type of classification with two outcomes.', 'start': 20642.52, 'duration': 2.421}, {'end': 20651.066, 'text': 'For example, it can be either true or false, or one or zero, or it can be yes or no.', 'start': 20645.442, 'duration': 5.624}, {'end': 20658.612, 'text': 'coming on to multi-class classification, the classification with more than two classes is known as multi-class classification,', 'start': 20651.066, 'duration': 7.546}, {'end': 20663.916, 'text': 'and in multi-class classification each sample is assigned to one and only one label or Target.', 'start': 20658.612, 'duration': 5.304}, {'end': 20666.621, 'text': 'Now talking about multi-label classification.', 'start': 20664.62, 'duration': 2.001}, {'end': 20672.545, 'text': 'This is a type of classification where each sample is assigned to a set of labels or targets.', 'start': 20666.882, 'duration': 5.663}, {'end': 20680.85, 'text': 'then we have initialize, which is used to assign the classifier to be used for the classification, and then we have trained the classifier which is.', 'start': 20672.545, 'duration': 8.305}], 'summary': 'Binary and multi-class classification explained. multi-label assigns samples to sets of labels.', 'duration': 40.332, 'max_score': 20640.518, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY20640518.jpg'}, {'end': 20730.479, 'src': 'embed', 'start': 20701.239, 'weight': 0, 'content': [{'end': 20703.08, 'text': 'or it can be cross validation, Etc.', 'start': 20701.239, 'duration': 1.841}, {'end': 20706.141, 'text': 'Now coming on to the types of learners in classification.', 'start': 20703.74, 'duration': 2.401}, {'end': 20707.541, 'text': 'We have two types of learners.', 'start': 20706.241, 'duration': 1.3}, {'end': 20709.762, 'text': 'We have lazy learners and eager learners.', 'start': 20707.781, 'duration': 1.981}, {'end': 20714.703, 'text': 'So lazy learners simply store the training data and wait until a testing data appears.', 'start': 20710.362, 'duration': 4.341}, {'end': 20719.694, 'text': 'The classification is done using the most related data in the stored training data.', 'start': 20715.492, 'duration': 4.202}, {'end': 20722.915, 'text': 'They have more predicting time compared to Eagle owners.', 'start': 20720.314, 'duration': 2.601}, {'end': 20730.479, 'text': 'For example, the K nearest neighbor or KNN algorithm, and we have case-based reasoning as well in the case of Eagle owners.', 'start': 20723.075, 'duration': 7.404}], 'summary': 'Lazy learners store training data, have longer prediction time compared to eager learners.', 'duration': 29.24, 'max_score': 20701.239, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY20701239.jpg'}, {'end': 21185.216, 'src': 'embed', 'start': 21157.856, 'weight': 11, 'content': [{'end': 21164.961, 'text': 'Also, they can be quite unstable, because even a simplistic change in the data can hinder the whole structure of the decision tree,', 'start': 21157.856, 'duration': 7.105}, {'end': 21166.122, 'text': 'talking about the use cases.', 'start': 21164.961, 'duration': 1.161}, {'end': 21168.884, 'text': 'It can be used for data exploration pattern recognition.', 'start': 21166.242, 'duration': 2.642}, {'end': 21174.668, 'text': 'We can use it for option pricing and finances and we can also use it for identifying disease and risk threats.', 'start': 21168.924, 'duration': 5.744}, {'end': 21176.649, 'text': 'So that is all about decision tree.', 'start': 21175.268, 'duration': 1.381}, {'end': 21178.611, 'text': "Let's take a look at random forest algorithm.", 'start': 21176.669, 'duration': 1.942}, {'end': 21185.216, 'text': 'So the random decision trees or random forest are an ensemble learning method for classification regression, etc.', 'start': 21179.451, 'duration': 5.765}], 'summary': 'Decision trees are unstable; used for data exploration, pattern recognition, option pricing, and identifying diseases and risk threats. random forest is an ensemble learning method.', 'duration': 27.36, 'max_score': 21157.856, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY21157856.jpg'}, {'end': 21636.78, 'src': 'embed', 'start': 21607.071, 'weight': 6, 'content': [{'end': 21608.512, 'text': "We'll do the classification over there.", 'start': 21607.071, 'duration': 1.441}, {'end': 21611.754, 'text': 'So I have opened the Jupiter notebook over here.', 'start': 21609.753, 'duration': 2.001}, {'end': 21613.496, 'text': 'I have named it as classification.', 'start': 21611.834, 'duration': 1.662}, {'end': 21621.641, 'text': "So I'll just name it as classification in machine learning or being decided as classification algorithms.", 'start': 21613.516, 'duration': 8.125}, {'end': 21627.846, 'text': 'So now first of all the first step into this problem statement is loading the MNIST data.', 'start': 21622.742, 'duration': 5.104}, {'end': 21631.168, 'text': "So first of all, I'm going to import the data set.", 'start': 21628.586, 'duration': 2.582}, {'end': 21636.78, 'text': 'As you can see I have no problem over here in executing this statement.', 'start': 21633.058, 'duration': 3.722}], 'summary': 'Using jupyter notebook to classify mnist data set.', 'duration': 29.709, 'max_score': 21607.071, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY21607071.jpg'}, {'end': 21711.55, 'src': 'embed', 'start': 21678.307, 'weight': 4, 'content': [{'end': 21683.23, 'text': "It might take some time because it's going to take the or load the data set from the internet.", 'start': 21678.307, 'duration': 4.923}, {'end': 21691.845, 'text': "So after this is finished, We will just explore the data, because that's the second part or the second step inside this building the model,", 'start': 21683.79, 'duration': 8.055}, {'end': 21692.825, 'text': 'the classification model.', 'start': 21691.845, 'duration': 0.98}, {'end': 21696.986, 'text': 'So after loading the data set you have to explore the data set.', 'start': 21693.405, 'duration': 3.581}, {'end': 21699.727, 'text': 'Okay Now the data set is loaded.', 'start': 21697.726, 'duration': 2.001}, {'end': 21701.827, 'text': "Let's see what all is there inside the data.", 'start': 21700.047, 'duration': 1.78}, {'end': 21711.55, 'text': "Okay, so data set contains a data which has an array and we have target we have feature names, which has I'm guessing for pixels until 784.", 'start': 21702.768, 'duration': 8.782}], 'summary': 'Data set loaded, exploring features for model building.', 'duration': 33.243, 'max_score': 21678.307, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY21678307.jpg'}], 'start': 19952.885, 'title': 'Machine learning problem solving', 'summary': 'Delves into problem-solving using machine learning, categorizing problems into five types and utilizing specific algorithms for each type, including classification, anomaly detection, regression, clustering, and reinforcement learning. it also provides an overview of various machine learning algorithms and covers the basics of classification, artificial neural networks, support vector machines, and classifier evaluation methods.', 'chapters': [{'end': 20204.029, 'start': 19952.885, 'title': 'Problem solving with machine learning', 'summary': 'Explains how to solve problems using machine learning, categorizing them into five types: a or b, weird, how much or how many, organized, and what to do next, and then using specific algorithms for each type, such as classification, anomaly detection, regression, clustering, and reinforcement learning.', 'duration': 251.144, 'highlights': ['Machine learning problems can be categorized into five types: A or B, weird, how much or how many, organized, and what to do next. The chapter categorizes machine learning problems into five types: A or B, weird, how much or how many, organized, and what should I do next.', 'Classification algorithms are used for problems with a set number of outputs, such as yes or no questions. Classification algorithms are used for problems with a set number of outputs, such as yes or no questions.', 'Anomaly detection algorithms are used for analyzing patterns and finding odd one out scenarios. Anomaly detection algorithms are used for analyzing patterns and finding odd one out scenarios.', 'Regression algorithms are applied when dealing with numeric values and wanting to predict a certain value. Regression algorithms are applied when dealing with numeric values and wanting to predict a certain value.', 'Clustering algorithms are used to understand the structure behind a certain dataset. Clustering algorithms are used to understand the structure behind a certain dataset.', 'Reinforcement learning algorithms are used for making decisions. Reinforcement learning algorithms are used for making decisions.']}, {'end': 20474.654, 'start': 20204.793, 'title': 'Machine learning algorithms overview', 'summary': 'Introduces various machine learning algorithms, including anomaly detection, regression, clustering, and reinforcement learning, with examples and use cases, emphasizing their applications in solving different types of problems.', 'duration': 269.861, 'highlights': ['Anomaly detection algorithms analyze patterns to detect unusual occurrences and are used in scenarios like credit card transaction monitoring, where deviations from regular transaction patterns trigger alerts for potential fraud detection. Anomaly detection algorithms are utilized to analyze patterns and identify anomalies, such as in credit card transaction monitoring, where unusual transactions trigger alerts for further investigation.', 'Regression algorithms are employed to predict numerical values, such as forecasting the temperature for the following day or determining the discount value to offer customers, ensuring profitability while attracting more business. Regression algorithms are used for predicting numerical values, like forecasting temperatures or determining optimal discount values for customers to balance profitability and customer attraction.', "Clustering algorithms, a part of unsupervised learning, help in organizing unstructured data into meaningful groups, enabling decision-making based on the established patterns, even though the computer comprehends the data as numerical values rather than its actual context. Clustering algorithms assist in organizing unstructured data into meaningful groups for decision-making, despite the computer's understanding being limited to numerical values rather than the actual context of the data.", 'Reinforcement algorithms enable decision-making based on past experiences, as seen in training computers for tasks like playing chess, where the decisions made are influenced by the model created through reinforcement learning. Reinforcement algorithms facilitate decision-making based on past experiences, as demonstrated in training computers for tasks like playing chess, where decisions are influenced by models created through reinforcement learning.']}, {'end': 21250.666, 'start': 20474.654, 'title': 'Classification in machine learning', 'summary': 'Covers the basics of classification in machine learning, including its definition, types of learners, classification algorithms such as logistic regression, naive bayes, stochastic gradient descent, k-nearest neighbor, decision tree, and random forest, along with their advantages, disadvantages, and use cases.', 'duration': 776.012, 'highlights': ['Classification is the process of categorizing a given set of data into classes, and it can be performed on both structured or unstructured data. It involves predicting the class of given data points and is often used in supervised learning. It includes both binary and multi-class classification, and the main goal is to identify which class or category the new data will fall into.', 'Logistic Regression is a classification algorithm in machine learning that is specifically meant for binary classification, using independent variables to determine an outcome. It quantitatively explains the factors leading to classification and is useful in understanding how a set of independent variables affect the outcome of the dependent variable. However, it only works when the predicted variable is binary.', "Naive Bayes classifier is based on Bayes' theorem and is known to outperform most of the classification methods in machine learning, particularly for comparatively large datasets. It requires a small amount of training data, is extremely fast, but is known to be a very bad estimator. It is commonly used in disease predictions, document classifications, spam filters, and sentiment analysis.", 'Stochastic Gradient Descent is particularly useful when the sample data is in a large number and supports different loss functions and penalties for classification. It has the advantage of ease of implementation and efficiency but requires a number of hyperparameters and is very sensitive to feature scaling. It is used in Internet of Things (IoT) and for updating parameters in neural networks or linear regression.', 'K-nearest neighbor algorithm stores all instances corresponding to training data in n-dimensional space and works by computing a simple majority vote of the k nearest neighbors of each point. It is simple in implementation and robust to noisy training data, but it requires determining the value of K and has a high computation cost. It is used in industrial applications, handwriting detection, image recognition, video recognition, and stock analysis.', 'Decision tree algorithm builds the classifier model in the form of a tree structure and utilizes the if-then rules, which are equally exhaustive and mutually exclusive. It has the advantage of simplicity in understanding and visualization, requires very little data preparation, but can create complex trees that may not categorize efficiently and can be quite unstable. It is used in data exploration, pattern recognition, option pricing, finances, and identifying disease and risk threats.', 'Random forest is an ensemble learning method that operates by constructing a multitude of decision trees at training time and outputs the class based on the mode of the classes or mean prediction of the individual trees. It is more accurate than decision trees due to the reduction in overfitting, but is complex in implementation and slow in real-time prediction. It is used for industrial applications, predicting mechanical part failures, social media share scores, and performance scores.']}, {'end': 21967.026, 'start': 21251.147, 'title': 'Artificial neural networks & support vector machines in machine learning', 'summary': 'Explores artificial neural networks and support vector machines, highlighting their advantages, disadvantages, use cases, and algorithm selection, as well as discussing classifier evaluation methods and algorithm selection.', 'duration': 715.879, 'highlights': ['Artificial Neural Networks: High tolerance to noisy data, ability to classify untrained patterns, and better performance with continuous valued inputs and outputs. Artificial Neural Networks have a high tolerance to noisy data, can classify untrained patterns, and perform better with continuous valued inputs and outputs.', 'Support Vector Machines: Efficient use of a subset of training points in the decision function, and highly effective in high dimensional spaces. Support Vector Machines efficiently use a subset of training points and are highly effective in high dimensional spaces.', "Classifier Evaluation: Includes methods such as holdout method, cross validation, classification report, and ROC curve, providing insights into accuracy, precision, recall, and the ROC curve's use for visual comparison of classification models. Classifier evaluation methods include holdout method, cross validation, classification report, and ROC curve, offering insights into accuracy, precision, recall, and the ROC curve's use for visual comparison of classification models.", 'Algorithm Selection: Steps involve reading the data, creating dependent and independent data sets, splitting the data into training and testing sets, training the model using different algorithms, evaluating the classifier, and choosing the classifier with the most accuracy. Algorithm selection includes steps such as reading the data, creating dependent and independent data sets, splitting the data, training the model, evaluating the classifier, and choosing the most accurate classifier.', 'MNIST Dataset: Consists of 70,000 small handwritten images labeled with respective digits, each with 784 features representing pixels density, and 28 by 28 pixel dimensions. The MNIST dataset comprises 70,000 small handwritten images with 784 features representing pixels density, and 28 by 28 pixel dimensions.']}], 'duration': 2014.141, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY19952885.jpg', 'highlights': ['Machine learning problems can be categorized into five types: A or B, weird, how much or how many, organized, and what to do next.', 'Classification algorithms are used for problems with a set number of outputs, such as yes or no questions.', 'Anomaly detection algorithms are used for analyzing patterns and finding odd one out scenarios.', 'Regression algorithms are applied when dealing with numeric values and wanting to predict a certain value.', 'Clustering algorithms are used to understand the structure behind a certain dataset.', 'Reinforcement learning algorithms are used for making decisions.', 'Anomaly detection algorithms analyze patterns to detect unusual occurrences and are used in scenarios like credit card transaction monitoring.', 'Regression algorithms are employed to predict numerical values, such as forecasting the temperature for the following day or determining the discount value to offer customers.', 'Clustering algorithms, a part of unsupervised learning, help in organizing unstructured data into meaningful groups.', 'Reinforcement algorithms enable decision-making based on past experiences, as seen in training computers for tasks like playing chess.', 'Classification is the process of categorizing a given set of data into classes, and it can be performed on both structured or unstructured data.', 'Logistic Regression is a classification algorithm in machine learning that is specifically meant for binary classification.', "Naive Bayes classifier is based on Bayes' theorem and is known to outperform most of the classification methods in machine learning.", 'Stochastic Gradient Descent is particularly useful when the sample data is in a large number and supports different loss functions and penalties for classification.', 'K-nearest neighbor algorithm stores all instances corresponding to training data in n-dimensional space and works by computing a simple majority vote of the k nearest neighbors of each point.', 'Decision tree algorithm builds the classifier model in the form of a tree structure and utilizes the if-then rules, which are equally exhaustive and mutually exclusive.', 'Random forest is an ensemble learning method that operates by constructing a multitude of decision trees at training time and outputs the class based on the mode of the classes or mean prediction of the individual trees.', 'Artificial Neural Networks: High tolerance to noisy data, ability to classify untrained patterns, and better performance with continuous valued inputs and outputs.', 'Support Vector Machines: Efficient use of a subset of training points in the decision function, and highly effective in high dimensional spaces.', "Classifier Evaluation: Includes methods such as holdout method, cross validation, classification report, and ROC curve, providing insights into accuracy, precision, recall, and the ROC curve's use for visual comparison of classification models.", 'Algorithm Selection: Steps involve reading the data, creating dependent and independent data sets, splitting the data into training and testing sets, training the model using different algorithms, evaluating the classifier, and choosing the classifier with the most accuracy.', 'MNIST Dataset: Consists of 70,000 small handwritten images labeled with respective digits, each with 784 features representing pixels density, and 28 by 28 pixel dimensions.']}, {'end': 24087.07, 'segs': [{'end': 22667.615, 'src': 'embed', 'start': 22641.986, 'weight': 2, 'content': [{'end': 22649.194, 'text': 'So starting with linear regression in simple linear regression, we are interested in things like y equal MX plus C.', 'start': 22641.986, 'duration': 7.208}, {'end': 22653.739, 'text': 'So what we are trying to find is the correlation between X and Y variable.', 'start': 22649.194, 'duration': 4.545}, {'end': 22658.567, 'text': 'This means that every value of X has a corresponding value of Y in it.', 'start': 22654.304, 'duration': 4.263}, {'end': 22662.751, 'text': 'If it is continuous, I like however and logistic regression.', 'start': 22658.768, 'duration': 3.983}, {'end': 22667.615, 'text': 'We are not fitting our data to a straight line like linear regression instead what we are doing.', 'start': 22662.951, 'duration': 4.664}], 'summary': "Linear regression correlates x and y, logistic regression doesn't fit data to a straight line.", 'duration': 25.629, 'max_score': 22641.986, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY22641986.jpg'}, {'end': 22928.015, 'src': 'embed', 'start': 22899.363, 'weight': 3, 'content': [{'end': 22903.185, 'text': 'For instance, if a company changes the price on a certain product several times,', 'start': 22899.363, 'duration': 3.822}, {'end': 22913.069, 'text': 'then it can record the quantity itself for each price level and then perform a linear regression with sole quantity as a dependent variable and price as the independent variable.', 'start': 22903.185, 'duration': 9.884}, {'end': 22920.292, 'text': 'This would result in a line that depicts the extent to which the customer reduce their consumption of the product as the price is increasing.', 'start': 22913.409, 'duration': 6.883}, {'end': 22923.433, 'text': 'So this result would help us in future pricing decisions.', 'start': 22920.672, 'duration': 2.761}, {'end': 22928.015, 'text': 'Next is assessment of risk and financial services and insurance domain.', 'start': 22924.093, 'duration': 3.922}], 'summary': 'Analyzing price changes using linear regression to predict customer consumption and inform pricing decisions. next, assessing risk in financial services and insurance.', 'duration': 28.652, 'max_score': 22899.363, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY22899363.jpg'}, {'end': 23169.403, 'src': 'embed', 'start': 23138.396, 'weight': 4, 'content': [{'end': 23141.397, 'text': "Now, let's move on and see the mathematical implementation of the things.", 'start': 23138.396, 'duration': 3.001}, {'end': 23142.118, 'text': 'All right.', 'start': 23141.898, 'duration': 0.22}, {'end': 23146.669, 'text': 'So we have x equal 1 2 3 4 5.', 'start': 23142.748, 'duration': 3.921}, {'end': 23148.19, 'text': "Let's plot them on the x-axis.", 'start': 23146.669, 'duration': 1.521}, {'end': 23151.528, 'text': 'So 0 1 2 3 4 5 6.', 'start': 23148.67, 'duration': 2.858}, {'end': 23155.831, 'text': 'All right, and we have y as 3 4 2 4 5.', 'start': 23151.531, 'duration': 4.3}, {'end': 23156.732, 'text': 'All right.', 'start': 23155.832, 'duration': 0.9}, {'end': 23160.253, 'text': "So let's plot 1 2 3 4 5 on the y-axis.", 'start': 23157.292, 'duration': 2.961}, {'end': 23162.934, 'text': "Now, let's plot coordinates one by one.", 'start': 23160.613, 'duration': 2.321}, {'end': 23165.795, 'text': 'So x equal 1 and y equal 3.', 'start': 23163.334, 'duration': 2.461}, {'end': 23169.403, 'text': 'So we have here x equal 1 and y equal 3.', 'start': 23165.795, 'duration': 3.608}], 'summary': 'Mathematical implementation of x and y plotted on axis.', 'duration': 31.007, 'max_score': 23138.396, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY23138396.jpg'}, {'end': 23415.113, 'src': 'embed', 'start': 23348.993, 'weight': 0, 'content': [{'end': 23361.986, 'text': '1 multiplied by 0.4, that is 0.4, and next 2 multiplied by 1.4, that is 2.8.', 'start': 23348.993, 'duration': 12.993}, {'end': 23364.989, 'text': 'All right now almost all the parts of our formula is done.', 'start': 23361.986, 'duration': 3.003}, {'end': 23369.153, 'text': 'So now what we need to do is get the summation of last two columns.', 'start': 23365.569, 'duration': 3.584}, {'end': 23369.853, 'text': 'All right.', 'start': 23369.613, 'duration': 0.24}, {'end': 23379.522, 'text': 'So the summation of X minus X bar whole square is 10 and the summation of X minus X bar multiplied by Y minus Y bar is 4.', 'start': 23370.354, 'duration': 9.168}, {'end': 23382.745, 'text': 'So the value of M will be equal to 4 by 10 fine.', 'start': 23379.522, 'duration': 3.223}, {'end': 23388.03, 'text': "So let's put this value of M equals 0.4 in our line y equal MX plus C.", 'start': 23383.486, 'duration': 4.544}, {'end': 23393.53, 'text': "So let's fill all the points into the equation and find the value of C.", 'start': 23389.085, 'duration': 4.445}, {'end': 23396.593, 'text': 'So we have Y as 3.6.', 'start': 23393.53, 'duration': 3.063}, {'end': 23406.863, 'text': 'Remember the mean way, M as 0.4, which we calculated just now, X as the mean value of X, that is 3, and we have the equation as 3.6 equals 0.4,', 'start': 23396.593, 'duration': 10.27}, {'end': 23408.105, 'text': 'multiplied by 3 plus C.', 'start': 23406.863, 'duration': 1.242}, {'end': 23408.365, 'text': 'All right.', 'start': 23408.105, 'duration': 0.26}, {'end': 23415.113, 'text': 'That is 3.6 equal 1.2 plus C.', 'start': 23411.952, 'duration': 3.161}], 'summary': 'Summation results in m = 0.4, y=3.6, and 3.6 = 1.2 + c.', 'duration': 66.12, 'max_score': 23348.993, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY23348993.jpg'}], 'start': 21967.026, 'title': 'Building predictors and classifier models', 'summary': 'Covers data shuffling, digit predictor creation, and model evaluation, achieving 97% accuracy with logistic regression and 90% with support vector machine in cross-validation, as well as providing a comprehensive overview of linear regression, including practical applications and model performance assessment using the r-squared method, yielding an example r square value of 0.63.', 'chapters': [{'end': 22096.744, 'start': 21967.026, 'title': 'Data shuffling and predictor creation', 'summary': 'Covers the process of shuffling the data, creating a digit predictor, and modifying output values, with a focus on ensuring the correct representation of output values within the system.', 'duration': 129.718, 'highlights': ["The data is shuffled using the 'shuffle index' method to ensure randomness and prevent bias in the training process. Shuffling the data to ensure randomness and prevent bias.", 'The process involves creating a predictor using the model and modifying output values to ensure correct representation within the system. Creating a predictor and modifying output values for correct representation.', 'The need to modify output values is emphasized through the process of addressing incorrect integer representation, ensuring accurate data handling. Emphasis on addressing incorrect integer representation for accurate data handling.']}, {'end': 22431.478, 'start': 22097.004, 'title': 'Mnist digit classification', 'summary': 'Discusses building a digit classifier using logistic regression and support vector machine models, achieving a 97% accuracy with logistic regression and 90% with support vector machine in cross-validation, and identifies logistic regression as the better performer due to the binomial nature of the outcome.', 'duration': 334.474, 'highlights': ['Logistic regression achieves 97% accuracy in cross-validation for digit classification. The logistic regression model achieves a 97% accuracy in cross-validation for digit classification, demonstrating its effectiveness in predicting digit values.', 'Support vector machine achieves 90% accuracy in cross-validation for digit classification. The support vector machine model achieves a 90% accuracy in cross-validation for digit classification, indicating its potential for accurate predictions but lower than logistic regression.', 'Logistic regression outperforms support vector machine due to binomial nature of the outcome. Logistic regression outperforms support vector machine in accuracy due to its effectiveness in handling binomial outcomes, making it a better choice for digit classification.']}, {'end': 23137.775, 'start': 22432.093, 'title': 'Linear regression overview', 'summary': "Covers the basics of linear regression, including its definition, uses, comparison with logistic regression, selection criteria, and practical applications, and provides a comprehensive understanding of the algorithm's mathematical implementation.", 'duration': 705.682, 'highlights': ['Linear regression is a statistical model that attempts to show the relationship between two variables with a linear equation. Linear regression is defined as a statistical model that aims to demonstrate the correlation between two variables using a linear equation.', 'The session covers the use cases of regression, including determining the strength of predictors, forecasting effects, and trend forecasting. The use cases of regression are explained, such as determining the strength of predictors, forecasting effects, and trend forecasting.', 'Comparison between linear and logistic regression is detailed, focusing on the type of function and output prediction of each method. The comparison between linear and logistic regression is provided, highlighting the differences in the type of function and output prediction of each method.', 'The criteria for selecting linear regression, including classification and regression capabilities, data quality, computational complexity, and comprehensibility, are outlined. The criteria for selecting linear regression are discussed, encompassing factors such as classification and regression capabilities, data quality, computational complexity, and comprehensibility.', 'Practical applications of linear regression are explained, such as evaluating trends and sales estimate, analyzing the impact of price changes, and assessing risk in financial services and insurance domain. Practical applications of linear regression are elaborated, including evaluating trends and sales estimate, analyzing the impact of price changes, and assessing risk in financial services and insurance domain.', 'A brief understanding of linear regression algorithm and its mathematical implementation is provided, emphasizing the creation of regression lines and the goal of error minimization. A brief understanding of the linear regression algorithm and its mathematical implementation is presented, focusing on the creation of regression lines and the objective of error minimization.']}, {'end': 23580.388, 'start': 23138.396, 'title': 'Linear regression and r-squared method', 'summary': 'Covers the implementation of linear regression using the least square method to find the best-fit line, with a detailed explanation of calculating the regression line equation, predicting values and assessing model performance using the r-squared method.', 'duration': 441.992, 'highlights': ['The chapter covers the implementation of linear regression using the least square method. It explains the method for finding the best-fit line and demonstrates the mathematical steps involved.', 'Calculating the equation of the regression line and predicting values using the least square method. It provides a detailed explanation of the mathematical process for calculating the regression line equation and predicting values based on the given M and C values.', "Assessing model performance using the R-squared method. It explains the R-squared method as a statistical measure of how well the data fit the regression line and the significance of R-squared value in evaluating the model's performance."]}, {'end': 24087.07, 'start': 23580.768, 'title': 'Calculating r square in linear regression', 'summary': 'Explains the process of calculating r square in linear regression, demonstrating the formula and mathematical steps involved, with an example yielding an r square value of 0.63, indicating a relatively good fit for the model.', 'duration': 506.302, 'highlights': ['The process of calculating R square in linear regression and demonstrating the formula and mathematical steps involved The chapter explains the process of calculating R square in linear regression, demonstrating the formula and mathematical steps involved.', 'Example yielding an R square value of 0.63, indicating a relatively good fit for the model The example presented in the transcript yields an R square value of 0.63, indicating a relatively good fit for the model.', 'Explanation of the formula for R square and the derivation of the R square value using the formula The transcript provides an explanation of the formula for R square and the derivation of the R square value using the formula.', 'Demonstration of the mathematical calculations involved in the R square calculation, including the total sum of squares and total sum of square of residuals The transcript demonstrates the mathematical calculations involved in the R square calculation, including the total sum of squares and total sum of square of residuals.']}], 'duration': 2120.044, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY21967026.jpg', 'highlights': ['Logistic regression achieves 97% accuracy in cross-validation for digit classification, demonstrating its effectiveness in predicting digit values.', 'Support vector machine achieves 90% accuracy in cross-validation for digit classification, indicating its potential for accurate predictions but lower than logistic regression.', 'The process involves creating a predictor using the model and modifying output values to ensure correct representation within the system.', 'The need to modify output values is emphasized through the process of addressing incorrect integer representation, ensuring accurate data handling.', 'Linear regression is defined as a statistical model that aims to demonstrate the correlation between two variables using a linear equation.', 'The use cases of regression are explained, such as determining the strength of predictors, forecasting effects, and trend forecasting.', 'Practical applications of linear regression are elaborated, including evaluating trends and sales estimate, analyzing the impact of price changes, and assessing risk in financial services and insurance domain.', 'The chapter covers the implementation of linear regression using the least square method, explaining the method for finding the best-fit line and demonstrating the mathematical steps involved.', "Assessing model performance using the R-squared method, explaining the R-squared method as a statistical measure of how well the data fit the regression line and the significance of R-squared value in evaluating the model's performance.", 'Example yielding an R square value of 0.63, indicating a relatively good fit for the model.']}, {'end': 25026.914, 'segs': [{'end': 24323.756, 'src': 'embed', 'start': 24294.701, 'weight': 0, 'content': [{'end': 24298.364, 'text': "So let's move forward and understand the what and why of logistic regression.", 'start': 24294.701, 'duration': 3.663}, {'end': 24304.641, 'text': 'Now this algorithm is most widely used when the dependent variable or you can say the output is in the binary format.', 'start': 24298.917, 'duration': 5.724}, {'end': 24308.765, 'text': 'So here you need to predict the outcome of a categorical dependent variable.', 'start': 24305.122, 'duration': 3.643}, {'end': 24312.708, 'text': 'So the outcome should be always discrete or categorical in nature.', 'start': 24309.185, 'duration': 3.523}, {'end': 24317.651, 'text': 'Now by discrete I mean the value should be binary or you can say you just have two values.', 'start': 24313.068, 'duration': 4.583}, {'end': 24319.613, 'text': 'It can either be 0 or 1.', 'start': 24317.772, 'duration': 1.841}, {'end': 24323.756, 'text': 'It can either be yes or no either be true or false or high or low.', 'start': 24319.613, 'duration': 4.143}], 'summary': 'Logistic regression predicts binary outcomes for categorical dependent variables.', 'duration': 29.055, 'max_score': 24294.701, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY24294701.jpg'}, {'end': 24493.845, 'src': 'embed', 'start': 24467.718, 'weight': 3, 'content': [{'end': 24474.739, 'text': "Let's say if it is more than my threshold value, it should give me the result as 1 if it is less than that then should give me the result as 0.", 'start': 24467.718, 'duration': 7.021}, {'end': 24476.74, 'text': 'So here my threshold value is 0.5.', 'start': 24474.739, 'duration': 2.001}, {'end': 24480.44, 'text': "I need to define that if my value let's say 0.8.", 'start': 24476.74, 'duration': 3.7}, {'end': 24482.201, 'text': 'It is more than 0.5.', 'start': 24480.44, 'duration': 1.761}, {'end': 24486.022, 'text': "Then the value shall be rounded off to 1 and let's say if it is less than 0.5.", 'start': 24482.201, 'duration': 3.821}, {'end': 24490.324, 'text': "Let's say I have a values 0.2 then should reduce it to 0.", 'start': 24486.022, 'duration': 4.302}, {'end': 24493.845, 'text': 'So here you can use the concept of threshold value to find your output.', 'start': 24490.324, 'duration': 3.521}], 'summary': 'Use threshold value of 0.5 to round values to 1 or 0 based on comparison.', 'duration': 26.127, 'max_score': 24467.718, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY24467718.jpg'}, {'end': 24539.839, 'src': 'embed', 'start': 24508.578, 'weight': 2, 'content': [{'end': 24511.8, 'text': "So let's see how an equation is formed to imitate this functionality.", 'start': 24508.578, 'duration': 3.222}, {'end': 24516.983, 'text': 'So over here, we have an equation of a straight line, which is y is equals to MX plus C.', 'start': 24512.3, 'duration': 4.683}, {'end': 24520.085, 'text': 'So in this case I just have only one independent variable.', 'start': 24516.983, 'duration': 3.102}, {'end': 24528.731, 'text': "but let's say, if we have many independent variable, then the equation becomes m1 x1 plus m2, x2 plus m3 x3 and so on till mn xn.", 'start': 24520.085, 'duration': 8.646}, {'end': 24531.153, 'text': 'Now, let us put in B and X.', 'start': 24529.191, 'duration': 1.962}, {'end': 24539.839, 'text': 'So here the equation becomes Y is equals to B 1 X 1 plus B 2 X 2 plus B 3 X 3 and so on till B N X N plus C.', 'start': 24531.153, 'duration': 8.686}], 'summary': 'Introduction to forming equations with multiple independent variables in the form of y = b1x1 + b2x2 + b3x3... + bnxn + c', 'duration': 31.261, 'max_score': 24508.578, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY24508578.jpg'}, {'end': 24858.826, 'src': 'embed', 'start': 24827.109, 'weight': 1, 'content': [{'end': 24830.97, 'text': "So let's move ahead and see some of the practical implementation of logistic regression.", 'start': 24827.109, 'duration': 3.861}, {'end': 24837.062, 'text': "So over here I'll be implementing two projects wherein I have the data set of a Titanic Over here,", 'start': 24831.63, 'duration': 5.432}, {'end': 24844.989, 'text': 'will predict what factors made people more likely to survive the sinking of the Titanic ship and in my second project will see the data analysis on the SUV cars.', 'start': 24837.062, 'duration': 7.927}, {'end': 24852.635, 'text': 'So over here we have the data of the SUV cars who can purchase it and what factors made people more interested in buying SUV.', 'start': 24845.389, 'duration': 7.246}, {'end': 24858.826, 'text': 'So these will be the major questions as to why you should implement logistic regression and what output will you get by it.', 'start': 24853.402, 'duration': 5.424}], 'summary': 'Practical implementation of logistic regression with titanic and suv car datasets.', 'duration': 31.717, 'max_score': 24827.109, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY24827109.jpg'}], 'start': 24087.77, 'title': 'Regression and logistic regression', 'summary': 'Covers implementing linear regression with scikit-learn, introducing regression and logistic regression, highlighting their applications, differences, and practical examples, demonstrating the ease of building machine learning models using scikit-learn, and emphasizing the predictive modeling techniques and practical applications of logistic regression.', 'chapters': [{'end': 24135.409, 'start': 24087.77, 'title': 'Implementing linear regression with scikit-learn', 'summary': 'Discusses implementing a linear regression model using the scikit-learn library in python, demonstrating how the code shortens and still yields the same r2 score, emphasizing the ease of building machine learning models using scikit-learn.', 'duration': 47.639, 'highlights': ['Implementing linear regression model using scikit-learn library in Python, showcasing the shortened code length while achieving the same R2 score.', 'Highlighting the ease of building machine learning models using scikit-learn library in Python.']}, {'end': 24490.324, 'start': 24135.859, 'title': 'Introduction to regression and logistic regression', 'summary': 'Provides an introduction to regression, discussing its predictive modeling technique, and explores the what and why of logistic regression, emphasizing its application in predicting binary outcomes and the transformation of values to adhere to the main rule of logistic regression. it also compares linear and logistic regression, highlighting the differences in the nature of the dependent variable and the need for threshold values in logistic regression.', 'duration': 354.465, 'highlights': ['Logistic regression is most widely used when the dependent variable is in binary format, requiring predictions of outcomes that are discrete or categorical in nature, such as 0 or 1, yes or no, true or false, high or low. Logistic regression is highlighted as the preferred method when predicting binary outcomes, where the dependent variable is constrained to two discrete values, providing a clear distinction from linear regression.', 'Logistic regression necessitates the transformation of values to adhere to the main rule, where the resulting curve must be formulated into an equation and follows the sigmoid function curve, converting any value from minus infinity to infinity to discrete binary values of 0 or 1. The process of transforming values to adhere to the main rule of logistic regression is explained, emphasizing the formulation of the resulting curve and the use of the sigmoid function curve to convert values into binary format.', 'The chapter explains the need for threshold values in logistic regression, where the probability of winning or losing is indicated, and values are rounded off based on the defined threshold, illustrating the practical implementation of logistic regression in making predictions. The concept of threshold values in logistic regression is highlighted, detailing their role in determining the outcome and rounding off values based on the specified threshold, providing practical insights into the application of logistic regression.']}, {'end': 25026.914, 'start': 24490.324, 'title': 'Understanding logistic regression', 'summary': 'Explains the concept of logistic regression, its equation, and differences from linear regression, as well as its practical applications, such as weather prediction and illness determination, and provides a practical implementation example with titanic data analysis and suv car data analysis.', 'duration': 536.59, 'highlights': ['Logistic regression helps in predicting discrete values, such as 0 or 1, and is used for classification problems, unlike linear regression which predicts continuous values. Logistic regression predicts categorical variables with discrete values, whereas linear regression solves regression problems with continuous variables.', 'Logistic regression is utilized in real-life scenarios, including weather prediction, multi-class classification in Python, and illness determination based on patient data. Logistic regression is applied in predicting weather conditions, multi-class classification, and determining illness severity using patient data features.', 'Practical implementation includes analyzing Titanic data to predict survival factors and SUV car data to understand purchase interest based on specific factors. Practical implementation involves analyzing Titanic data to predict survival factors and using SUV car data to understand purchase interest based on specific factors.']}], 'duration': 939.144, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY24087770.jpg', 'highlights': ['Demonstrates ease of building ML models using scikit-learn in Python', 'Implementing linear regression model with scikit-learn library in Python', 'Logistic regression predicts categorical variables with discrete values', 'Logistic regression is utilized in real-life scenarios like weather prediction', 'Practical implementation involves analyzing Titanic data to predict survival factors']}, {'end': 25856.076, 'segs': [{'end': 25290.642, 'src': 'embed', 'start': 25265.702, 'weight': 0, 'content': [{'end': 25271.665, 'text': 'so you can simply explore your data set by making use of various columns, and then you can plot a graph between them.', 'start': 25265.702, 'duration': 5.963}, {'end': 25273.826, 'text': 'So you can either plot a correlation graph.', 'start': 25271.685, 'duration': 2.141}, {'end': 25275.327, 'text': 'You can plot a distribution graph.', 'start': 25273.866, 'duration': 1.461}, {'end': 25276.567, 'text': "It's up to you guys.", 'start': 25275.647, 'duration': 0.92}, {'end': 25280.969, 'text': 'So let me just go back to my Jupiter notebook and let me analyze some of the data over here.', 'start': 25276.867, 'duration': 4.102}, {'end': 25282.73, 'text': 'My second part is to analyze data.', 'start': 25281.009, 'duration': 1.721}, {'end': 25290.642, 'text': 'So I just put this in header 2 Now to put this in here to I just have to go on code click on Markdown and I just run this suppose.', 'start': 25282.97, 'duration': 7.672}], 'summary': 'Explore and plot data in jupyter notebook for analysis.', 'duration': 24.94, 'max_score': 25265.702, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY25265702.jpg'}, {'end': 25405.183, 'src': 'embed', 'start': 25363.718, 'weight': 2, 'content': [{'end': 25367.221, 'text': "So I'm using Titanic data set and let me just run this.", 'start': 25363.718, 'duration': 3.503}, {'end': 25369.422, 'text': "Okay, I've done a mistake over here.", 'start': 25367.241, 'duration': 2.181}, {'end': 25375.46, 'text': 'So over here you can see I have survived column on the x-axis and I have the count on the Y now.', 'start': 25370.137, 'duration': 5.323}, {'end': 25379.682, 'text': 'So here your blue color stands for your male passengers and orange stands for your female.', 'start': 25375.74, 'duration': 3.942}, {'end': 25384.989, 'text': 'So, as you can see here the passengers who did not survive, that has a value 0.', 'start': 25380.323, 'duration': 4.666}, {'end': 25388.371, 'text': 'So we can see that majority of males did not survive.', 'start': 25384.989, 'duration': 3.382}, {'end': 25392.774, 'text': 'and if we see the people who survived here, we can see the majority of females survive.', 'start': 25388.371, 'duration': 4.403}, {'end': 25395.616, 'text': 'So this basically concludes the gender of the survival rate.', 'start': 25393.115, 'duration': 2.501}, {'end': 25401.46, 'text': 'So it appears on average women were more than three times more likely to survive than men next.', 'start': 25395.937, 'duration': 5.523}, {'end': 25405.183, 'text': 'Let us plot another plot where we have the hue as the passenger class.', 'start': 25401.5, 'duration': 3.683}], 'summary': 'On average, women were over three times more likely to survive than men. the survival rate by gender and passenger class is demonstrated.', 'duration': 41.465, 'max_score': 25363.718, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY25363718.jpg'}, {'end': 25655.928, 'src': 'embed', 'start': 25619.288, 'weight': 5, 'content': [{'end': 25624.471, 'text': 'we have already discussed, as in the people who tend to travel in the first class usually pay the highest fare.', 'start': 25619.288, 'duration': 5.183}, {'end': 25626.672, 'text': 'then we have the cabin number and we have embarked.', 'start': 25624.471, 'duration': 2.201}, {'end': 25630.394, 'text': 'So these are the columns that will be doing data wrangling on.', 'start': 25627.072, 'duration': 3.322}, {'end': 25636.918, 'text': 'so we have analyzed the data and we have seen quite a few graphs in which we can conclude which variable is better than the another,', 'start': 25630.394, 'duration': 6.524}, {'end': 25638.159, 'text': 'or what is the relationship they hold.', 'start': 25636.918, 'duration': 1.241}, {'end': 25640.535, 'text': 'So third step is my data wrangling.', 'start': 25638.914, 'duration': 1.621}, {'end': 25643.298, 'text': 'So data wrangling basically means cleaning your data.', 'start': 25640.856, 'duration': 2.442}, {'end': 25648.582, 'text': 'So if you have a large data set you might be having some null values or you can say nan values.', 'start': 25643.658, 'duration': 4.924}, {'end': 25653.386, 'text': "So it's very important that you remove all the unnecessary items that are present in your data set.", 'start': 25648.962, 'duration': 4.424}, {'end': 25655.928, 'text': 'So removing this directly affects your accuracy.', 'start': 25653.706, 'duration': 2.222}], 'summary': 'First class travelers pay highest fare, cleaning data is crucial for accuracy.', 'duration': 36.64, 'max_score': 25619.288, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY25619288.jpg'}, {'end': 25726.325, 'src': 'embed', 'start': 25699.895, 'weight': 3, 'content': [{'end': 25704.817, 'text': 'So falls is where the value is not null and true is where the value is not over here.', 'start': 25699.895, 'duration': 4.922}, {'end': 25706.237, 'text': 'You can see in the cabin column.', 'start': 25704.837, 'duration': 1.4}, {'end': 25708.198, 'text': 'We have the very first value which is null.', 'start': 25706.257, 'duration': 1.941}, {'end': 25717.661, 'text': 'So we have to do something on this so you can see that we have a large data set the counting does not stop and we can actually see the sum of it.', 'start': 25708.578, 'duration': 9.083}, {'end': 25721.843, 'text': 'We can actually print the number of passengers who have the nan value in each column.', 'start': 25717.741, 'duration': 4.102}, {'end': 25726.325, 'text': "So I'll say Titanic underscore data is null and I want the sum of it.", 'start': 25722.103, 'duration': 4.222}], 'summary': 'Identify and sum null values in titanic data.', 'duration': 26.43, 'max_score': 25699.895, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY25699895.jpg'}], 'start': 25027.414, 'title': 'Titanic data analysis', 'summary': 'Covers logistic regression data analysis with 891 passengers, data analysis process including plots for relationship between variables, survival rate, gender distribution, passenger class distribution, age distribution, and fare distribution in the titanic dataset, and an overview of the columns with data wrangling steps.', 'chapters': [{'end': 25250.856, 'start': 25027.414, 'title': 'Logistic regression data analysis', 'summary': 'Discusses the five steps of logistic regression data analysis, including collecting data, importing libraries, and analyzing the titanic dataset with 891 passengers.', 'duration': 223.442, 'highlights': ['The chapter discusses the five steps of logistic regression data analysis It mentions the five steps involved in logistic regression data analysis, emphasizing the process of data analysis, data wrangling, model building, testing, and accuracy checking.', 'Analyzing the Titanic dataset with 891 passengers It provides a specific example of analyzing the Titanic dataset, including importing libraries like pandas, numpy, seaborn, and matplotlib, and printing the number of passengers in the dataset, which is 891.', 'Emphasizing the process of data analysis, data wrangling, model building, testing, and accuracy checking It emphasizes the essential steps in logistic regression data analysis, which include data analysis, data wrangling to clean the data, building and testing the model, and checking the accuracy of the values.', 'Importing libraries like pandas, numpy, seaborn, and matplotlib It specifically mentions the libraries used in the data analysis process, including pandas for data analysis, numpy for numerical computations, seaborn for statistical plotting, and matplotlib for plotting.', 'Printing the number of passengers in the dataset, which is 891 It quantifies the number of passengers in the Titanic dataset, providing a specific figure of 891 passengers.']}, {'end': 25535.698, 'start': 25251.477, 'title': 'Data analysis process', 'summary': 'Covers the process of analyzing data, including creating plots to check the relationship between variables, analyzing the survival rate, gender distribution, passenger class distribution, age distribution, and fare distribution in the titanic dataset.', 'duration': 284.221, 'highlights': ['The majority of passengers did not survive, with around 550 non-survivors and approximately 350 survivors, indicating very few survivors compared to non-survivors. Around 550 passengers did not survive, while approximately 350 passengers survived, indicating a significant difference in the number of survivors and non-survivors in the Titanic dataset.', 'On average, women were more than three times more likely to survive than men based on the gender distribution in the dataset. The analysis shows that women were over three times more likely to survive than men based on the gender distribution in the Titanic dataset.', 'Passengers in the first and second classes had a higher survival rate compared to those in the third class, with a larger number of non-survivors from the third class. The analysis indicates that passengers in the higher classes (first and second) had a higher survival rate compared to those in the third class, with a larger number of non-survivors from the third class.', 'The age distribution in the dataset shows a higher number of young passengers (0-10 years) and average age passengers, with a decreasing population as age increases. The analysis of the age distribution reveals a higher number of young passengers (0-10 years) and average age passengers, with a decreasing population as age increases in the Titanic dataset.', 'The fare distribution analysis indicates that the majority of fares fall within the range of 0 to 100, with the highest frequency occurring within this range. The fare distribution analysis reveals that the majority of fares fall within the range of 0 to 100, with the highest frequency occurring within this range in the Titanic dataset.']}, {'end': 25856.076, 'start': 25536.378, 'title': 'Titanic data analysis', 'summary': 'Provides an overview of the columns in the titanic dataset, including analysis of survival rates, gender, passenger class, age distribution, and data wrangling steps such as checking for null values and removing unnecessary columns.', 'duration': 319.698, 'highlights': ['Passenger class and survival analysis The chapter includes analysis of survival rates based on passenger class, where it is observed that passengers traveling in first and second class tend to be older than those in the third class.', 'Data wrangling and handling missing values The chapter covers the process of data wrangling, emphasizing the importance of cleaning the dataset by removing null values and unnecessary columns, with specific focus on the analysis of missing values using heat maps and strategies for handling them.', 'Sibling/Spouse analysis The chapter discusses the analysis of the number of siblings or spouses aboard the Titanic, highlighting the distribution of these values and the conclusion that the majority of passengers had neither children nor a spouse on board.']}], 'duration': 828.662, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY25027414.jpg', 'highlights': ['The chapter discusses the five steps of logistic regression data analysis', 'Analyzing the Titanic dataset with 891 passengers', 'Emphasizing the process of data analysis, data wrangling, model building, testing, and accuracy checking', 'Importing libraries like pandas, numpy, seaborn, and matplotlib', 'Printing the number of passengers in the dataset, which is 891', 'The majority of passengers did not survive, with around 550 non-survivors and approximately 350 survivors', 'On average, women were more than three times more likely to survive than men based on the gender distribution in the dataset', 'Passengers in the first and second classes had a higher survival rate compared to those in the third class, with a larger number of non-survivors from the third class', 'The age distribution in the dataset shows a higher number of young passengers (0-10 years) and average age passengers, with a decreasing population as age increases', 'The fare distribution analysis indicates that the majority of fares fall within the range of 0 to 100, with the highest frequency occurring within this range', 'Passenger class and survival analysis', 'Data wrangling and handling missing values', 'Sibling/Spouse analysis']}, {'end': 27209.264, 'segs': [{'end': 26259.406, 'src': 'embed', 'start': 26230.384, 'weight': 2, 'content': [{'end': 26232.785, 'text': 'We have to concatenate mbar and pcl.', 'start': 26230.384, 'duration': 2.401}, {'end': 26235.267, 'text': "And then I'll mention the access to one.", 'start': 26233.526, 'duration': 1.741}, {'end': 26243, 'text': "I'll just run this can you to print the head? So over here, you can see that these columns have been added over here.", 'start': 26236.078, 'duration': 6.922}, {'end': 26251.883, 'text': "So we have the mail column which basically tells whether person is male or it's a female then we have the embark which is basically Q and S.", 'start': 26243.561, 'duration': 8.322}, {'end': 26257.445, 'text': "So if it's traveling from Kunz town value would be 1, else it would be 0, and if both of these values are 0,", 'start': 26251.883, 'duration': 5.562}, {'end': 26259.406, 'text': 'it is definitely traveling from chair Bob.', 'start': 26257.445, 'duration': 1.961}], 'summary': 'Concatenated mbar and pcl, added columns for male/female, embark, and travel destination.', 'duration': 29.022, 'max_score': 26230.384, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY26230384.jpg'}, {'end': 26861.484, 'src': 'embed', 'start': 26827.658, 'weight': 1, 'content': [{'end': 26835.543, 'text': 'I have gender as male and female then we have the age we have the estimated salary and then we have the purchase column.', 'start': 26827.658, 'duration': 7.885}, {'end': 26838.926, 'text': 'So this is my discrete column or you can say the categorical column.', 'start': 26835.603, 'duration': 3.323}, {'end': 26845.631, 'text': 'So here we just have the value that is 0 and 1 and this column we need to predict whether a person can actually purchase a SUV or not.', 'start': 26838.966, 'duration': 6.665}, {'end': 26850.735, 'text': 'So based on these factors, we will be deciding whether a person can actually purchase a SUV or not.', 'start': 26846.072, 'duration': 4.663}, {'end': 26852.937, 'text': 'So we know the salary of a person.', 'start': 26851.436, 'duration': 1.501}, {'end': 26858.101, 'text': 'We know the age and using these we can predict whether person can actually purchase SUV or not.', 'start': 26852.997, 'duration': 5.104}, {'end': 26861.484, 'text': 'Let me just go to my Jupyter notebook and it is implement logistic regression.', 'start': 26858.481, 'duration': 3.003}], 'summary': 'Using gender, age, and salary to predict suv purchase with logistic regression.', 'duration': 33.826, 'max_score': 26827.658, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY26827658.jpg'}, {'end': 27193.47, 'src': 'embed', 'start': 27165.004, 'weight': 0, 'content': [{'end': 27167.905, 'text': 'We have predicted the values and now we want to know the accuracy.', 'start': 27165.004, 'duration': 2.901}, {'end': 27171.766, 'text': 'So to know the accuracy first we need to import accuracy score.', 'start': 27168.405, 'duration': 3.361}, {'end': 27182.88, 'text': "So I'll say from sklearn.metrics import accuracy score and using this function we can calculate the accuracy or you can manually do that by creating a confusion matrix.", 'start': 27171.806, 'duration': 11.074}, {'end': 27186.624, 'text': 'So I just pass in my y test and my y predicted.', 'start': 27183.541, 'duration': 3.083}, {'end': 27193.47, 'text': 'All right, so over here I get the accuracy is 89% so we want to know the accuracy in percentage.', 'start': 27186.644, 'duration': 6.826}], 'summary': 'The accuracy of the prediction is 89%.', 'duration': 28.466, 'max_score': 27165.004, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY27165004.jpg'}], 'start': 25856.076, 'title': 'Logistic regression and model training', 'summary': 'Covers data wrangling techniques, encoding categorical variables, and training a logistic regression model using sklearn, achieving a 70-30 split ratio for training and testing subsets. it also includes the evaluation of model performance, achieving an 89% accuracy in predicting suv purchases.', 'chapters': [{'end': 26069.015, 'start': 25856.076, 'title': 'Data wrangling and logistic regression', 'summary': "Covers data wrangling techniques including imputation, dropping columns and handling null values, and the process of converting string values to categorical variables in order to implement logistic regression for prediction, with a focus on applying logistic regression to the 'survived' column in the titanic dataset.", 'duration': 212.939, 'highlights': ['The chapter covers data wrangling techniques including imputation, dropping columns and handling null values. The method of imputation is discussed for handling null values in the dataset, along with the process of dropping columns and removing null values.', 'The process of converting string values to categorical variables in order to implement logistic regression for prediction is explained. The process of converting string values to categorical variables using Pandas, in order to implement logistic regression for prediction, is described.', "Applying logistic regression to the 'survived' column in the Titanic dataset is emphasized. The focus is on applying logistic regression to the 'survived' column in the Titanic dataset, with the objective of predicting the number of people who survived and those who did not."]}, {'end': 26562.223, 'start': 26069.615, 'title': 'Data wrangling & model training', 'summary': 'Covers data wrangling techniques, including encoding categorical variables and dropping irrelevant columns, and then proceeds to train a logistic regression model using sklearn, achieving a split ratio of 70-30 for training and testing subsets.', 'duration': 492.608, 'highlights': ['The chapter introduces data wrangling techniques such as encoding categorical variables and dropping irrelevant columns to prepare the dataset for model training. categorical variables, irrelevant columns', "The process involves encoding categorical variables using pandas get_dummies function, such as encoding the 'sex' column with 0 and 1 representing male and female, and dropping the first column to avoid multicollinearity. encoding 'sex' column, using 0 and 1, dropping first column", "The transcript demonstrates the application of get_dummies function to encode categorical variables like 'embarked' and 'P class', providing a detailed explanation of the process and its outcomes. application of get_dummies function, encoding 'embarked' and 'P class'", "The chapter includes the steps for splitting the dataset into training and testing subsets using sklearn's train_test_split function, achieving a split ratio of 70-30 for training and testing subsets. split ratio of 70-30", 'The process involves training a logistic regression model using sklearn, creating an instance of the logistic regression model and fitting the model to the training data. training a logistic regression model']}, {'end': 27209.264, 'start': 26562.927, 'title': 'Model performance evaluation and logistic regression', 'summary': 'Covers the evaluation of model performance including accuracy, classification report, and confusion matrix, with a detailed explanation of logistic regression for predicting suv purchases achieving an 89% accuracy.', 'duration': 646.337, 'highlights': ['The chapter covers the evaluation of model performance including accuracy, classification report, and confusion matrix. The chapter discusses methods to evaluate model performance, including accuracy calculation, classification report, and confusion matrix, emphasizing the importance of these metrics in assessing model accuracy.', 'Detailed explanation of logistic regression for predicting SUV purchases achieving an 89% accuracy. The detailed explanation of using logistic regression to predict SUV purchases, achieving an accuracy of 89%, by defining independent and dependent variables, splitting the data, scaling input values, applying logistic regression, and calculating accuracy using the accuracy score function.']}], 'duration': 1353.188, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY25856076.jpg', 'highlights': ['The chapter covers the evaluation of model performance including accuracy, classification report, and confusion matrix.', 'The detailed explanation of using logistic regression to predict SUV purchases, achieving an accuracy of 89%, by defining independent and dependent variables, splitting the data, scaling input values, applying logistic regression, and calculating accuracy using the accuracy score function.', "The process involves encoding categorical variables using pandas get_dummies function, such as encoding the 'sex' column with 0 and 1 representing male and female, and dropping the first column to avoid multicollinearity.", "The chapter includes the steps for splitting the dataset into training and testing subsets using sklearn's train_test_split function, achieving a split ratio of 70-30 for training and testing subsets.", 'The process involves training a logistic regression model using sklearn, creating an instance of the logistic regression model and fitting the model to the training data.']}, {'end': 28357.589, 'segs': [{'end': 27331.218, 'src': 'embed', 'start': 27301.907, 'weight': 2, 'content': [{'end': 27303.368, 'text': 'whereas the bottom two pixels are white.', 'start': 27301.907, 'duration': 1.461}, {'end': 27308.152, 'text': "Now what happens, we'll divide these pixels and we'll send these pixels to each and every node.", 'start': 27303.749, 'duration': 4.403}, {'end': 27309.854, 'text': 'So for that we need four nodes.', 'start': 27308.553, 'duration': 1.301}, {'end': 27315.2, 'text': 'So this particular pixel will go to this node, it will go to this node, this pixel will go to this node and finally,', 'start': 27310.314, 'duration': 4.886}, {'end': 27318.384, 'text': "this pixel will go to this particular node that I'm highlighting with my cursor.", 'start': 27315.2, 'duration': 3.184}, {'end': 27321.307, 'text': 'Now what happens? We provide them random weights.', 'start': 27318.905, 'duration': 2.402}, {'end': 27326.333, 'text': 'So these white lines actually represent the positive weights and these black lines represents the negative weights.', 'start': 27321.728, 'duration': 4.605}, {'end': 27331.218, 'text': 'Now this particular brightness when we display high brightness will consider it as negative.', 'start': 27326.974, 'duration': 4.244}], 'summary': 'Dividing pixels into four nodes with random weights for processing.', 'duration': 29.311, 'max_score': 27301.907, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY27301907.jpg'}, {'end': 27434.955, 'src': 'embed', 'start': 27409.166, 'weight': 6, 'content': [{'end': 27413.889, 'text': "So what I'll do, I'll actually calculate the inverse by providing a negative weight like this over here.", 'start': 27409.166, 'duration': 4.723}, {'end': 27415.07, 'text': "I've provided a negative weight.", 'start': 27413.929, 'duration': 1.141}, {'end': 27415.671, 'text': 'It will come up.', 'start': 27415.15, 'duration': 0.521}, {'end': 27419.612, 'text': 'So when I provide a positive weight, so it will stay wherever it is.', 'start': 27416.051, 'duration': 3.561}, {'end': 27426.813, 'text': 'after that it will detect and the output you can see will be a horizontal image, not a solid, not a vertical, not a diagonal, but a horizontal,', 'start': 27419.612, 'duration': 7.201}, {'end': 27432.955, 'text': 'and after that we are going to calculate the difference between the actual output and the desired output and we are going to update the weights accordingly.', 'start': 27426.813, 'duration': 6.142}, {'end': 27434.955, 'text': 'Now, this is just an example guys.', 'start': 27433.475, 'duration': 1.48}], 'summary': 'Calculating inverse using weights, adjusting based on output difference. example only.', 'duration': 25.789, 'max_score': 27409.166, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY27409166.jpg'}, {'end': 27534.423, 'src': 'embed', 'start': 27510.395, 'weight': 3, 'content': [{'end': 27516.597, 'text': 'Then what happens, we fixate these patterns of local contrast in order to form the face features such as eyes, nose, ears, et cetera.', 'start': 27510.395, 'duration': 6.202}, {'end': 27521.419, 'text': 'And then we accumulate these features for the correct face and then we determine the image.', 'start': 27517.077, 'duration': 4.342}, {'end': 27525.62, 'text': 'So this is how a deep learning network or you can say deep network looks like.', 'start': 27521.979, 'duration': 3.641}, {'end': 27528.641, 'text': "And I'll give you some applications of deep learning.", 'start': 27526.62, 'duration': 2.021}, {'end': 27530.661, 'text': 'So here are a few applications of deep learning.', 'start': 27529.181, 'duration': 1.48}, {'end': 27532.462, 'text': 'It can be used in self-driving cars.', 'start': 27530.701, 'duration': 1.761}, {'end': 27534.423, 'text': 'So you must have heard about self-driving cars.', 'start': 27532.862, 'duration': 1.561}], 'summary': 'Deep learning networks identify features to determine images. applications include self-driving cars.', 'duration': 24.028, 'max_score': 27510.395, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY27510395.jpg'}, {'end': 27872.611, 'src': 'embed', 'start': 27846.736, 'weight': 0, 'content': [{'end': 27852.119, 'text': 'Well in my opinion, this is very important for anyone who wants to know more about Keras or better.', 'start': 27846.736, 'duration': 5.383}, {'end': 27854.921, 'text': 'They want to start creating their own neural nets using Keras.', 'start': 27852.179, 'duration': 2.742}, {'end': 27858.022, 'text': 'So clearly Keras is an API designed for humans.', 'start': 27855.381, 'duration': 2.641}, {'end': 27859.483, 'text': 'Well, why so?', 'start': 27858.423, 'duration': 1.06}, {'end': 27867.368, 'text': 'because it follows the best practices for reducing cognitive load, which ensures that the models are consistent and the corresponding APIs are simple.', 'start': 27859.483, 'duration': 7.885}, {'end': 27872.611, 'text': 'and moving on, Kiras provides clear feedback upon occurrence of any error,', 'start': 27868.067, 'duration': 4.544}], 'summary': 'Keras is important for creating neural nets, designed for humans, with clear feedback upon errors.', 'duration': 25.875, 'max_score': 27846.736, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY27846736.jpg'}, {'end': 28300.624, 'src': 'embed', 'start': 28273.818, 'weight': 7, 'content': [{'end': 28277.319, 'text': 'which is also called as a log loss in most cases, and so on.', 'start': 28273.818, 'duration': 3.501}, {'end': 28282.661, 'text': 'and the last step is to actually train the network based on the input data, which is also called as a training data,', 'start': 28277.319, 'duration': 5.342}, {'end': 28288.123, 'text': 'and after training we will need to test the model based on the trained data to check if the model actually learnt anything.', 'start': 28282.661, 'duration': 5.462}, {'end': 28290.203, 'text': 'So it is as simple as this guys.', 'start': 28288.483, 'duration': 1.72}, {'end': 28292.984, 'text': 'What do you think I would love to know your views on this.', 'start': 28290.363, 'duration': 2.621}, {'end': 28294.225, 'text': 'So head to the comment section.', 'start': 28293.044, 'duration': 1.181}, {'end': 28295.525, 'text': "Let's have an interaction there.", 'start': 28294.345, 'duration': 1.18}, {'end': 28298.182, 'text': "And now guys, let's spice things up a bit.", 'start': 28296.141, 'duration': 2.041}, {'end': 28300.624, 'text': "I'm sure you guys were curious about the use case.", 'start': 28298.303, 'duration': 2.321}], 'summary': "Training and testing a model involves using input data and evaluating the model's learning.", 'duration': 26.806, 'max_score': 28273.818, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY28273818.jpg'}], 'start': 27209.264, 'title': 'Understanding deep learning and keras', 'summary': 'Introduces deep learning and its applications, keras functionalities and industry impact, user experience, and model flexibility, as well as keras execution types and neural network implementation, including a use case of building a wine classifier.', 'chapters': [{'end': 27573.889, 'start': 27209.264, 'title': 'Understanding deep learning and its applications', 'summary': 'Introduces the concept of deep learning, explaining the structure of deep networks with multiple hidden layers and its applications, including self-driving cars, voice control assistance, and automatic image caption generation.', 'duration': 364.625, 'highlights': ['Deep learning is implemented by the help of deep networks and deep networks are neural networks with multiple hidden layers. Deep learning is achieved through neural networks with multiple hidden layers, enabling complex problem-solving.', 'The chapter explains the structure of deep networks with multiple hidden layers, illustrating the process of input layers determining patterns of local contrast and fixing face features in subsequent layers. The process of input layers determining patterns of local contrast and fixing face features in subsequent layers is described, outlining the progression of deep network processing.', 'Applications of deep learning include self-driving cars, voice control assistance (e.g., Siri), and automatic image caption generation based on uploaded images. Deep learning is applied in self-driving cars, voice control assistance (e.g., Siri), and automatic image caption generation, showcasing its diverse practical applications.']}, {'end': 27825.373, 'start': 27574.659, 'title': 'Understanding keras: a deep dive', 'summary': 'Delves into the functionalities and industry impact of keras, a python-based deep learning framework that has gained significant traction, with over 250,000 active developers and usage by major firms like netflix and uber, backed by industry giants such as microsoft, google, nvidia, and amazon.', 'duration': 250.714, 'highlights': ['Keras runs on top of Tiano TensorFlow or CNTK, making it simple to work with by enabling easy model building through stacking layers and connecting graphs. Keras runs on top of Tiano TensorFlow or CNTK, making it simple to work with by enabling easy model building through stacking layers and connecting graphs.', 'Keras is open source and actively developed by a large community of over 250,000 active developers, with extensive documentation and high performance due to its usage to specify and train differentiable programs. Keras is open source and actively developed by a large community of over 250,000 active developers, with extensive documentation and high performance due to its usage to specify and train differentiable programs.', 'Keras has garnered industry traction and is used by major firms like Netflix, Uber, and Expedia, and has received contributions from industry giants such as Microsoft, Google, Nvidia, and Amazon. Keras has garnered industry traction and is used by major firms like Netflix, Uber, and Expedia, and has received contributions from industry giants such as Microsoft, Google, Nvidia, and Amazon.', 'Keras is multi-backend and supports multi-platform, has an excellent research and production community, easy-to-grasp concepts, fast processing, and seamless support for both CPU and GPU, as well as the freedom to design on any architecture. Keras is multi-backend and supports multi-platform, has an excellent research and production community, easy-to-grasp concepts, fast processing, and seamless support for both CPU and GPU, as well as the freedom to design on any architecture.']}, {'end': 28141.205, 'start': 27825.973, 'title': 'Understanding keras: user experience and model flexibility', 'summary': 'Explains how keras simplifies model production for beginners, provides a user-friendly api, supports multi-platform development, and introduces the concepts of computational graph and two major models - sequential and functional.', 'duration': 315.232, 'highlights': ['Keras provides an API designed for humans, reducing cognitive load and ensuring consistency in models and APIs. Keras follows best practices to reduce cognitive load and ensures consistent models and simple APIs, making it easier for users to work with.', 'Keras integrates with lower level deep learning framework languages like TensorFlow, CNTK, Theano, or MXNet. Keras allows implementation of models built in base languages like TensorFlow, CNTK, Theano, or MXNet, enhancing flexibility for developers.', 'Keras supports multi-platform development in Python and can run with TensorFlow, CNTK, Theano, or MXNet, on CPU or GPU, including support for Nvidia and AMD. Keras supports multi-platform development in Python and can run with various frameworks on CPU or GPU, including support for Nvidia and AMD.', 'The chapter explains the concept of computational graph, which is useful for calculating derivatives during backpropagation and implementing distributed computing. Computational graphs are used for calculating derivatives during backpropagation and implementing distributed computing, simplifying complex expressions.', 'The chapter introduces two major models in Keras - sequential model and functional model, with explanations of their key features and applications. The chapter introduces the sequential and functional models in Keras, explaining their features and applications, along with code examples.']}, {'end': 28357.589, 'start': 28142.233, 'title': 'Keras execution and neural network implementation', 'summary': 'Explains keras execution types, steps to implement a neural network using keras, and a use case of building a wine classifier, emphasizing the five major steps involved and the suitability of wide and deep learning networks for the problem statement.', 'duration': 215.356, 'highlights': ['The chapter explains two basic types of execution in Keras: deferred and eager execution, with eager execution utilizing Python runtime as the execution runtime for all models, similar to execution with numpy.', 'It details the five major steps in implementing a neural network using Keras, including preparing inputs, defining the neural network model, specifying the optimizer, defining the loss function, and training the network based on the input data.', 'The use case of building a wine classifier with Keras functional API and tensorflow, emphasizing the suitability of wide and deep learning networks for predicting the price of a bottle of wine based on its description and variety.']}], 'duration': 1148.325, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY27209264.jpg', 'highlights': ['Deep learning is achieved through neural networks with multiple hidden layers, enabling complex problem-solving.', 'Applications of deep learning include self-driving cars, voice control assistance (e.g., Siri), and automatic image caption generation.', 'Keras is open source and actively developed by a large community of over 250,000 active developers, with extensive documentation and high performance.', 'Keras has garnered industry traction and is used by major firms like Netflix, Uber, and Expedia, and has received contributions from industry giants such as Microsoft, Google, Nvidia, and Amazon.', 'Keras provides an API designed for humans, reducing cognitive load and ensuring consistency in models and APIs.', 'Keras integrates with lower level deep learning framework languages like TensorFlow, CNTK, Theano, or MXNet, enhancing flexibility for developers.', 'The chapter introduces the sequential and functional models in Keras, explaining their features and applications, along with code examples.', 'It details the five major steps in implementing a neural network using Keras, including preparing inputs, defining the neural network model, specifying the optimizer, defining the loss function, and training the network based on the input data.', 'The use case of building a wine classifier with Keras functional API and tensorflow, emphasizing the suitability of wide and deep learning networks for predicting the price of a bottle of wine based on its description and variety.']}, {'end': 29241.211, 'segs': [{'end': 29129.673, 'src': 'embed', 'start': 29067.027, 'weight': 0, 'content': [{'end': 29070.168, 'text': "So guys here's the important thing that you have to notice with every Epoch.", 'start': 29067.027, 'duration': 3.141}, {'end': 29080.353, 'text': 'We were actually reducing the loss all the way from 1100 to 130 guys, and the accuracy of prediction went from 0.02 all the way to 0.0994,', 'start': 29070.208, 'duration': 10.145}, {'end': 29082.174, 'text': 'which is almost 0.1..', 'start': 29080.353, 'duration': 1.821}, {'end': 29085.335, 'text': "Well, wow, that's definitely a breakthrough for just 10 passes guys.", 'start': 29082.174, 'duration': 3.161}, {'end': 29087.645, 'text': 'And now that the training is done.', 'start': 29086.224, 'duration': 1.421}, {'end': 29088.947, 'text': "It's time to evaluate it.", 'start': 29087.766, 'duration': 1.181}, {'end': 29091.149, 'text': 'So let me go ahead and run this piece of code for you guys.', 'start': 29088.987, 'duration': 2.162}, {'end': 29093.231, 'text': 'So that was quick.', 'start': 29092.51, 'duration': 0.721}, {'end': 29099.376, 'text': "that took only about 5 seconds and we have evaluated the model and now it's time for the most important part, guys,", 'start': 29093.231, 'duration': 6.145}, {'end': 29103.94, 'text': 'seeing how our model actually performs on the data that it has never seen before, to do.', 'start': 29099.376, 'duration': 4.564}, {'end': 29108.284, 'text': "this will actually call the predict function on our trained model and we'll be passing it our data set.", 'start': 29103.94, 'duration': 4.344}, {'end': 29109.846, 'text': "So let's go ahead and do just that.", 'start': 29108.605, 'duration': 1.241}, {'end': 29113.606, 'text': "Well now that that's done.", 'start': 29112.486, 'duration': 1.12}, {'end': 29118.328, 'text': "We'll have to compare the predictions to the actual values for the force 15 wines from our test data set.", 'start': 29113.647, 'duration': 4.681}, {'end': 29123.19, 'text': 'So guys as you can see we have a set of predictions from the description and the predicted value is about $24.', 'start': 29118.749, 'duration': 4.441}, {'end': 29126.632, 'text': 'Well, the actual value is $22 next up.', 'start': 29123.19, 'duration': 3.442}, {'end': 29129.673, 'text': 'We have $34 as a predicted one while the average is 70.', 'start': 29126.672, 'duration': 3.001}], 'summary': 'Reduced loss from 1100 to 130, accuracy increased to 0.0994 after 10 passes. model evaluation completed in 5 seconds.', 'duration': 62.646, 'max_score': 29067.027, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY29067027.jpg'}], 'start': 28358.009, 'title': 'Wine data analysis and price prediction model', 'summary': 'Introduces the wine data set from kaggle with 12 columns, aiming to create a model for wine variety, winery, and location prediction. it also details building a wine price prediction model using python, achieving an average prediction difference of $10 and an accuracy of 0.0994 with tensorflow version 1.7.', 'chapters': [{'end': 28504.259, 'start': 28358.009, 'title': 'Wine data set analysis', 'summary': 'Introduces the wine data set from kaggle, which consists of 12 columns of data, aiming to create a model to identify the variety, winery, and location of a wine based on the description alone, offering great opportunities for sentiment analysis and other text-related predictive models.', 'duration': 146.25, 'highlights': ["The wine data set from Kaggle consists of 12 columns of data including country, description, designation, points, price, province, region one, region two, taster's name, Twitter handle, title, variety, and winery. The data set includes 12 columns, providing detailed information about the wine, such as country, description, points, and price.", 'The goal is to create a model that can identify the variety, winery, and location of a wine based on the description alone, and the data set offers opportunities for sentiment analysis and other text-related predictive models. The main objective is to build a model that can predict the variety, winery, and location of a wine solely based on its description, presenting opportunities for sentiment analysis and other predictive models.', "The output of the model is the pricing that it predicts based on the textual information provided, offering a practical application for the model. The model provides pricing predictions based on the textual information, demonstrating a practical application for the model's output.", 'The wide and deep models are explained, with wide models having sparse feature vectors and deep networks excelling in tasks like speech and image recognition. The wide and deep models are described, highlighting the characteristics of wide models with sparse feature vectors and the capabilities of deep networks in tasks such as speech and image recognition.']}, {'end': 29241.211, 'start': 28504.779, 'title': 'Building a wine price prediction model', 'summary': 'Details the step-by-step process of building a wine price prediction model using python, pandas, numpy, scikit-learn, and tensorflow, and demonstrates the training and evaluation of the model, achieving an average prediction difference of $10 for every wine bottle and an accuracy of 0.0994 with tensorflow version 1.7.', 'duration': 736.432, 'highlights': ['The chapter details the step-by-step process of building a wine price prediction model using Python, Pandas, NumPy, Scikit-learn, and TensorFlow. Building a model using Python, Pandas, NumPy, Scikit-learn, and TensorFlow.', 'The model achieves an average prediction difference of $10 for every wine bottle and an accuracy of 0.0994 with TensorFlow version 1.7. Average prediction difference of $10 for every wine bottle, accuracy of 0.0994 with TensorFlow version 1.7.', 'The model training process involves 10 epochs, with each epoch reducing the loss from 1100 to 130 and improving the accuracy from 0.02 to 0.0994. Model training involving 10 epochs, loss reduction from 1100 to 130, accuracy improvement from 0.02 to 0.0994.']}], 'duration': 883.202, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY28358009.jpg', 'highlights': ["The wine data set from Kaggle consists of 12 columns of data including country, description, designation, points, price, province, region one, region two, taster's name, Twitter handle, title, variety, and winery, providing detailed information about the wine.", 'The goal is to create a model that can predict the variety, winery, and location of a wine based on its description, presenting opportunities for sentiment analysis and other predictive models.', "The model provides pricing predictions based on the textual information, demonstrating a practical application for the model's output.", 'The wide and deep models are described, highlighting the characteristics of wide models with sparse feature vectors and the capabilities of deep networks in tasks such as speech and image recognition.', 'The chapter details the step-by-step process of building a wine price prediction model using Python, Pandas, NumPy, Scikit-learn, and TensorFlow.', 'The model achieves an average prediction difference of $10 for every wine bottle and an accuracy of 0.0994 with TensorFlow version 1.7.', 'Model training involving 10 epochs, loss reduction from 1100 to 130, accuracy improvement from 0.02 to 0.0994.']}, {'end': 31065.883, 'segs': [{'end': 30164.444, 'src': 'embed', 'start': 30129.546, 'weight': 2, 'content': [{'end': 30132.707, 'text': 'the value that will be fed back to the placeholder B will be two comma four.', 'start': 30129.546, 'duration': 3.161}, {'end': 30135.408, 'text': 'Now let me execute this practically in my PyCharm.', 'start': 30133.347, 'duration': 2.061}, {'end': 30138.489, 'text': 'Let me remove all of this, all right.', 'start': 30136.728, 'duration': 1.761}, {'end': 30147.991, 'text': "So I'll define a placeholder A, tf.placeholder, which is of float 32 bits.", 'start': 30139.585, 'duration': 8.406}, {'end': 30156.278, 'text': 'Similarly, B will be again a placeholder of tf.float 32 bits.', 'start': 30148.792, 'duration': 7.486}, {'end': 30164.444, 'text': 'Then we define one more node, adder underscore node, which is nothing but the addition of these two placeholders.', 'start': 30156.998, 'duration': 7.446}], 'summary': 'Two placeholders a and b of float 32 bits are defined, followed by the addition of these two placeholders in tensorflow.', 'duration': 34.898, 'max_score': 30129.546, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY30129546.jpg'}, {'end': 30350.107, 'src': 'embed', 'start': 30322.364, 'weight': 0, 'content': [{'end': 30325.227, 'text': 'So obviously we cannot increase the efficiency without knowing the error.', 'start': 30322.364, 'duration': 2.863}, {'end': 30328.369, 'text': "Let's understand the general flow of how we increase the efficiency.", 'start': 30325.647, 'duration': 2.722}, {'end': 30331.912, 'text': 'So first we create the model as you have seen in the previous slide.', 'start': 30329.01, 'duration': 2.902}, {'end': 30333.814, 'text': 'Then we calculate the loss.', 'start': 30332.553, 'duration': 1.261}, {'end': 30337.697, 'text': "Loss is basically how far our model's output is from the actual output.", 'start': 30334.334, 'duration': 3.363}, {'end': 30341.22, 'text': 'Then we try to reduce the loss by updating the variables.', 'start': 30338.177, 'duration': 3.043}, {'end': 30346.724, 'text': 'Then again we check the loss and update the variables and this process keeps on repeating until the loss becomes minimum.', 'start': 30341.68, 'duration': 5.044}, {'end': 30350.107, 'text': 'And now is the time to understand how we calculate loss.', 'start': 30347.224, 'duration': 2.883}], 'summary': 'Efficiency increases through minimizing loss in model output.', 'duration': 27.743, 'max_score': 30322.364, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY30322364.jpg'}, {'end': 30683.076, 'src': 'embed', 'start': 30650.096, 'weight': 1, 'content': [{'end': 30651.237, 'text': 'So the first value of x is one.', 'start': 30650.096, 'duration': 1.141}, {'end': 30655.975, 'text': 'So let me take that first and the value of b is minus one.', 'start': 30652.111, 'duration': 3.864}, {'end': 30663.304, 'text': 'So minus one into one plus one is equal to zero.', 'start': 30655.996, 'duration': 7.308}, {'end': 30665.987, 'text': 'So the value of our linear underscore model is zero.', 'start': 30663.464, 'duration': 2.523}, {'end': 30671.132, 'text': 'And over here also the value of y that is our placeholder or the actual output is also zero.', 'start': 30666.607, 'duration': 4.525}, {'end': 30683.076, 'text': 'Let us take the next value of x, which is nothing but two, minus one into two plus one is equal to what? Minus one, which is again equal to y.', 'start': 30671.892, 'duration': 11.184}], 'summary': 'Linear model output: y = -1x + 1, y = 0 when x = 1, y = -1 when x = 2', 'duration': 32.98, 'max_score': 30650.096, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY30650096.jpg'}], 'start': 29241.811, 'title': 'Tensorflow basics and model optimization', 'summary': 'Covers the basics of tensorflow including data types, model definition, and graph manipulation, as well as optimizing a model using gradient descent to reduce loss, achieving increasing accuracy in classifying sonar data to distinguish between a rock and a mine.', 'chapters': [{'end': 29466.533, 'start': 29241.811, 'title': 'Tensorflow model for sonar data', 'summary': 'Introduces the sonar data set, containing 111 patterns bounced off a metal cylinder and 97 patterns bounced off a rock, to be used for training a tensorflow model to classify patterns as either a rock or a mine. the model is trained, and its accuracy is observed to increase with each iteration.', 'duration': 224.722, 'highlights': ['The sonar data set contains 111 patterns bounced off a metal cylinder and 97 patterns bounced off a rock, both at various angles and conditions, to be used for training the TensorFlow model.', 'The last column of every record in the data set represents a label which represents the name of the class, either a rock or a mine.', 'The steps involved in implementing the TensorFlow use case include processing the data set, defining features and labels, dividing the data set into training and testing parts, training the model, reducing the error, testing the model on the test data, and calculating the accuracy.', 'The deep learning model is trained and its accuracy is observed to increase with every iteration, aiming to achieve the highest possible accuracy.', 'TensorFlow programs use tensors to represent data, where tensors are described by a unit of dimensionality known as rank, and a rank two tensor is typically a matrix while a rank one tensor is a vector.']}, {'end': 29637.6, 'start': 29467.389, 'title': 'Tensor data types & tensorflow basics', 'summary': 'Explains tensor data types including integer, float, string, and boolean types, and then delves into the basics of tensorflow, which is an open-source software library released in 2015 by google to make it easier for developers to design, develop, and train deep learning models on neural networks, working by first defining and describing the model in abstract, and then running it in a session.', 'duration': 170.211, 'highlights': ['TensorFlow is an open source software library released in 2015 by Google to make it easier for developers to design, develop and train deep learning models on neural networks. The chapter introduces TensorFlow as an open-source software library released in 2015 by Google to make it easier for developers to design, develop, and train deep learning models on neural networks.', 'TensorFlow works by first defining and describing our model in abstract, and then we are ready we make it a reality in a session. The chapter explains that TensorFlow works by first defining and describing the model in abstract and then making it a reality in a session.', 'The TensorFlow code basics are explained, consisting of two sections: building the computation graph and running the session. The chapter details the basics of TensorFlow code, consisting of two sections: building the computation graph and running the session.', 'TensorFlow is nothing but the combination of two words tensor and flow, which is nothing but the tensors flowing through a computation graph. The chapter explains that TensorFlow is a combination of two words: tensor and flow, representing the flow of tensors through a computation graph.', "In TensorFlow, you don't need to specify the tensor data type as it will automatically assign the correct type, but if you want to save the reserve memory, you can specify the tensor data type to be of 32 bits. The chapter mentions that in TensorFlow, there is no need to specify the tensor data type as it will automatically assign the correct type, but specifying the tensor data type to be 32 bits can save memory."]}, {'end': 30020.681, 'start': 29638.061, 'title': 'Executing and visualizing tensorflow graphs', 'summary': 'Explains how to execute and visualize tensorflow graphs, showcasing methods to run sessions, close sessions, and visualize graphs using tensorboard, resulting in the successful execution of computations and visualization of the graph.', 'duration': 382.62, 'highlights': ['The chapter explains how to execute and visualize TensorFlow graphs The transcript provides a detailed explanation of executing and visualizing TensorFlow graphs, encompassing various methods and techniques.', 'Showcasing methods to run sessions, close sessions, and visualize graphs using TensorBoard The chapter demonstrates the process of running sessions, closing sessions, and visualizing graphs using TensorBoard, providing practical examples and commands for each step.', 'Successful execution of computations and visualization of the graph The transcript highlights the successful execution of computations within sessions and the visualization of the graph using TensorBoard, with specific output values and the creation of graph directories for visualization.']}, {'end': 30589.539, 'start': 30020.721, 'title': 'Tensorflow basics and graph manipulation', 'summary': 'Explains the basics of tensorflow, including constants, placeholders, and variables, and demonstrates the manipulation of a computation graph, with key points including the usage of constants, placeholders, and variables and their role in model training.', 'duration': 568.818, 'highlights': ['The chapter explains the basics of TensorFlow, including constants, placeholders, and variables The explanation covers constant nodes with specific values, placeholder promises to provide values later, and the usage of variables for model training.', 'Demonstrates the manipulation of a computation graph The demonstration includes initializing variables, launching the graph, running sessions, feeding values, and calculating loss to train the model.', 'Usage of constants, placeholders, and variables and their role in model training The chapter illustrates the use of constants to produce constant results, placeholders to accept external inputs, and variables for training model by updating parameters.']}, {'end': 31065.883, 'start': 30589.539, 'title': 'Optimizing model with gradient descent', 'summary': 'Explains the concept of reducing loss by updating variables using the gradient descent optimizer, demonstrating its use with an analogy and mathematical explanation, and applying it in a model with the code and its execution in pycharm.', 'duration': 476.344, 'highlights': ['The chapter explains the concept of reducing loss by updating variables using the gradient descent optimizer By changing the value of variables, the loss can be reduced, demonstrated with specific examples and calculations.', 'Demonstrating use with an analogy and mathematical explanation An analogy of reaching a lake from a mountain while blindfolded is used to explain the concept, followed by a mathematical explanation of the gradient descent optimizer.', 'Applying the gradient descent optimizer in a model with the code and its execution in PyCharm The process of applying the gradient descent optimizer in a model is shown with code and its execution in PyCharm, resulting in updated variable values.']}], 'duration': 1824.072, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY29241811.jpg', 'highlights': ['The deep learning model is trained and its accuracy is observed to increase with every iteration, aiming to achieve the highest possible accuracy.', 'The sonar data set contains 111 patterns bounced off a metal cylinder and 97 patterns bounced off a rock, both at various angles and conditions, to be used for training the TensorFlow model.', 'The chapter explains the concept of reducing loss by updating variables using the gradient descent optimizer.', 'TensorFlow is an open source software library released in 2015 by Google to make it easier for developers to design, develop, and train deep learning models on neural networks.', 'The chapter explains how to execute and visualize TensorFlow graphs.']}, {'end': 31885.014, 'segs': [{'end': 31341.22, 'src': 'embed', 'start': 31301.934, 'weight': 3, 'content': [{'end': 31304.014, 'text': 'So this is where we have performed the one hot encoding.', 'start': 31301.934, 'duration': 2.08}, {'end': 31306.615, 'text': 'Then what we are gonna do, we are gonna read the data set.', 'start': 31304.574, 'duration': 2.041}, {'end': 31310.696, 'text': 'So X is basically a feature and Y is one hot encoded label.', 'start': 31306.815, 'duration': 3.881}, {'end': 31312.8, 'text': "Then we're gonna shuffle the data set.", 'start': 31311.499, 'duration': 1.301}, {'end': 31317.564, 'text': 'We need to shuffle the data set because the whole data set that we have, it is present in an order.', 'start': 31312.82, 'duration': 4.744}, {'end': 31320.747, 'text': 'For example, in the beginning we have mines and then we have rocks.', 'start': 31317.964, 'duration': 2.783}, {'end': 31322.028, 'text': 'So we need to shuffle it.', 'start': 31321.167, 'duration': 0.861}, {'end': 31326.532, 'text': "So after doing that, we're gonna split the data set into two parts, training and testing.", 'start': 31322.608, 'duration': 3.924}, {'end': 31329.074, 'text': 'How are we gonna do that? You can see it over here.', 'start': 31326.552, 'duration': 2.522}, {'end': 31335.019, 'text': 'So we have defined the test size as .20, which means the 20% of the data set will be your testing data set.', 'start': 31329.174, 'duration': 5.845}, {'end': 31341.22, 'text': "Now you can go ahead and inspect the shape of the training and the testing data, although it's not necessary.", 'start': 31336.007, 'duration': 5.213}], 'summary': 'Performed one hot encoding, shuffled & split dataset into 80% training & 20% testing data.', 'duration': 39.286, 'max_score': 31301.934, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY31301934.jpg'}, {'end': 31451.559, 'src': 'embed', 'start': 31412.818, 'weight': 2, 'content': [{'end': 31415.48, 'text': 'So this is nothing but an example of multi-layer perceptron.', 'start': 31412.818, 'duration': 2.662}, {'end': 31420.785, 'text': 'So X is our placeholder in which we are going to feed in the input values, or you can say the data set.', 'start': 31416.161, 'duration': 4.624}, {'end': 31426.511, 'text': 'it is offload 32 bits and then the shape of this particular tensor is none comma ending.', 'start': 31420.785, 'duration': 5.726}, {'end': 31428.853, 'text': 'So ending is what we have seen earlier.', 'start': 31427.011, 'duration': 1.842}, {'end': 31434.618, 'text': "So if I write here none, which means it can be any value, then we'll define a variable W,", 'start': 31429.253, 'duration': 5.365}, {'end': 31441.024, 'text': "which will be initialized with zeros and it'll have shape and underscore dim, which we know what it is, and an underscore class,", 'start': 31434.618, 'duration': 6.406}, {'end': 31443.952, 'text': 'which is nothing but Class, to which a pattern represents.', 'start': 31441.024, 'duration': 2.928}, {'end': 31445.033, 'text': 'So we have two classes here.', 'start': 31443.972, 'duration': 1.061}, {'end': 31447.135, 'text': "So it'll be n underscore dim comma two.", 'start': 31445.093, 'duration': 2.042}, {'end': 31449.036, 'text': 'Then we have one more variable b.', 'start': 31447.775, 'duration': 1.261}, {'end': 31451.559, 'text': "We'll fill it with zeros or we'll initialize it with zeros.", 'start': 31449.036, 'duration': 2.523}], 'summary': 'Example of multi-layer perceptron with placeholder x, 32-bit offload, w initialized with zeros, and classes n_dim and 2.', 'duration': 38.741, 'max_score': 31412.818, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY31412818.jpg'}, {'end': 31885.014, 'src': 'embed', 'start': 31852.637, 'weight': 0, 'content': [{'end': 31857.521, 'text': "So basically we'll be providing some input to this particular model and it'll predict the outcome for us.", 'start': 31852.637, 'duration': 4.884}, {'end': 31860.283, 'text': "So it'll predict whether it is a rock or a mine.", 'start': 31857.901, 'duration': 2.382}, {'end': 31862.645, 'text': 'So the whole code remains the same.', 'start': 31860.824, 'duration': 1.821}, {'end': 31867.55, 'text': "It's just that you need to provide the model path which you have done when you were actually defining the model and training it.", 'start': 31863.106, 'duration': 4.444}, {'end': 31873.731, 'text': 'And then what you need to do is you need to again create one saver object and after that, finally,', 'start': 31868.21, 'duration': 5.521}, {'end': 31878.212, 'text': 'you need to call this restore function in order to restore the model that we have over here.', 'start': 31873.731, 'duration': 4.481}, {'end': 31885.014, 'text': "You're going to provide two arguments one is says and another is nothing but your model underscore path, which we have given in the previous step.", 'start': 31878.232, 'duration': 6.782}], 'summary': 'A model predicts rock or mine based on input.', 'duration': 32.377, 'max_score': 31852.637, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY31852637.jpg'}], 'start': 31066.343, 'title': 'Implementing and training neville mine identifier model', 'summary': 'Details the implementation of the neville mine identifier (nmi) use case using tensorflow, achieving a test accuracy of 85% and an average mean squared error of 24.4505 after training the model for 1,000 epochs.', 'chapters': [{'end': 31563.614, 'start': 31066.343, 'title': 'Implementing neville mine identifier', 'summary': 'Details the implementation of the neville mine identifier (nmi) use case, involving data processing, encoding, model implementation using tensorflow, and training, aiming to achieve an accurate model for identifying mines and rocks.', 'duration': 497.271, 'highlights': ['The use case involves processing the dataset, defining features and labels, encoding the dependent variable, and dividing the dataset into training and testing sets. The use case involves steps like processing the dataset, defining features and labels, and encoding the dependent variable, followed by division of the dataset into training and testing sets.', 'Explanation of label encoding and one-hot encoding for the dependent variable, with a practical example. The explanation of label encoding and one-hot encoding is provided, with a practical example demonstrating the encoding process.', 'Definition of important parameters and variables for working with tensors, including learning rate, epoch, loss function, and model path. Important parameters and variables for working with tensors are defined, including learning rate, epoch, loss function, and model path.']}, {'end': 31885.014, 'start': 31564.214, 'title': 'Training and testing model', 'summary': 'Covers the process of defining a cost function, using a gradient descent optimizer with a learning rate of 0.03, training the model for 1,000 epochs, achieving a test accuracy of 85%, with an average mean squared error of 24.4505, and saving the model for future use.', 'duration': 320.8, 'highlights': ['Training the model for 1,000 epochs The model is trained for 1,000 epochs, repeating the training process 1,000 times.', "Achieving a test accuracy of 85% The model achieves a test accuracy of 85%, indicating the accuracy of the model's predictions on the test data.", 'Using a gradient descent optimizer with a learning rate of 0.03 The optimization process utilizes a gradient descent optimizer with a learning rate of 0.03 to minimize the cost function or loss.']}], 'duration': 818.671, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY31066343.jpg', 'highlights': ["Achieving a test accuracy of 85% The model achieves a test accuracy of 85%, indicating the accuracy of the model's predictions on the test data.", 'Training the model for 1,000 epochs The model is trained for 1,000 epochs, repeating the training process 1,000 times.', 'Using a gradient descent optimizer with a learning rate of 0.03 The optimization process utilizes a gradient descent optimizer with a learning rate of 0.03 to minimize the cost function or loss.', 'Explanation of label encoding and one-hot encoding for the dependent variable, with a practical example. The explanation of label encoding and one-hot encoding is provided, with a practical example demonstrating the encoding process.', 'Definition of important parameters and variables for working with tensors, including learning rate, epoch, loss function, and model path. Important parameters and variables for working with tensors are defined, including learning rate, epoch, loss function, and model path.', 'The use case involves processing the dataset, defining features and labels, encoding the dependent variable, and dividing the dataset into training and testing sets. The use case involves steps like processing the dataset, defining features and labels, and encoding the dependent variable, followed by division of the dataset into training and testing sets.']}, {'end': 33763.902, 'segs': [{'end': 32656.012, 'src': 'embed', 'start': 32628.575, 'weight': 4, 'content': [{'end': 32634.339, 'text': 'So you can see here all the words are in the lower case and all of them are separated with the help of a space bar.', 'start': 32628.575, 'duration': 5.764}, {'end': 32640.963, 'text': "Now there's another transformation, which is known as the flat map, to give you a flat and output,", 'start': 32635.519, 'duration': 5.444}, {'end': 32643.744, 'text': "and I'm passing the same function which I created earlier.", 'start': 32640.963, 'duration': 2.781}, {'end': 32646.886, 'text': "So let's go ahead and have a look at the output for this one.", 'start': 32644.085, 'duration': 2.801}, {'end': 32656.012, 'text': 'So as you can see here, we got the first five elements which are the same one as we got here the contrast transactions and anti records.', 'start': 32647.327, 'duration': 8.685}], 'summary': 'Using flat map transformation to flatten output after lowercasing and separating words with space.', 'duration': 27.437, 'max_score': 32628.575, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY32628575.jpg'}, {'end': 32803.881, 'src': 'embed', 'start': 32775.658, 'weight': 2, 'content': [{'end': 32779.979, 'text': 'Now in Spark, we perform parallel processing through the help of shared variables, or,', 'start': 32775.658, 'duration': 4.321}, {'end': 32787.121, 'text': 'when the driver sends any tasks with the executor present on the cluster, a copy of the shared variable is also sent to the each node of the cluster,', 'start': 32779.979, 'duration': 7.142}, {'end': 32789.542, 'text': 'thus maintaining high availability and fault tolerance.', 'start': 32787.121, 'duration': 2.421}, {'end': 32795.459, 'text': 'Now this is done in order to accomplish the task and Apache Spark supports two type of shared variables.', 'start': 32790.222, 'duration': 5.237}, {'end': 32803.881, 'text': 'One of them is broadcast and the other one is the accumulator now broadcast variables are used to save the copy of data on all the notes in a cluster.', 'start': 32796.099, 'duration': 7.782}], 'summary': 'Apache spark uses shared variables for parallel processing, with broadcast and accumulator variables for fault tolerance and high availability.', 'duration': 28.223, 'max_score': 32775.658, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY32775658.jpg'}, {'end': 32972.266, 'src': 'embed', 'start': 32947.074, 'weight': 3, 'content': [{'end': 32952.074, 'text': 'Now, let me show you how to create a data frame in PySpark and perform various actions and transformations on it.', 'start': 32947.074, 'duration': 5}, {'end': 32956.356, 'text': "So let's continue this in the same notebook, which we have here.", 'start': 32953.114, 'duration': 3.242}, {'end': 32965.442, 'text': "Now here we have taken the NYC flight data and I'm creating a data frame, which is the NYC flights underscore TF not to load the data.", 'start': 32956.997, 'duration': 8.445}, {'end': 32972.266, 'text': 'We are using the spark dot read dot CSV method and you to provide the path which is the local path by default.', 'start': 32965.482, 'duration': 6.784}], 'summary': 'Creating a data frame in pyspark with nyc flight data for analysis.', 'duration': 25.192, 'max_score': 32947.074, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY32947074.jpg'}, {'end': 33360.915, 'src': 'embed', 'start': 33333.014, 'weight': 1, 'content': [{'end': 33337.337, 'text': 'as well as real-time streaming analytics, a various algorithm supported by this.', 'start': 33333.014, 'duration': 4.323}, {'end': 33341.54, 'text': 'libraries are first of all, we have the spark.ml live now.', 'start': 33337.337, 'duration': 4.203}, {'end': 33347.845, 'text': 'recently, the spy spark mlips supports model-based collaborative filtering by a small set of latent factors,', 'start': 33341.54, 'duration': 6.305}, {'end': 33353.33, 'text': 'and here all the users and the products are described which we can use to predict the missing entries.', 'start': 33347.845, 'duration': 5.485}, {'end': 33360.915, 'text': 'However to learn these latent factors spark.mlb uses the alternating least square, which is the ALS algorithm.', 'start': 33353.788, 'duration': 7.127}], 'summary': 'Real-time streaming analytics with spark.mlips supports model-based collaborative filtering using als algorithm.', 'duration': 27.901, 'max_score': 33333.014, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY33333014.jpg'}, {'end': 33473.657, 'src': 'embed', 'start': 33438.012, 'weight': 0, 'content': [{'end': 33443.921, 'text': 'Now here we are going to use a heart disease prediction model and we are going to predict it using the decision tree,', 'start': 33438.012, 'duration': 5.909}, {'end': 33446.665, 'text': 'with the help of classification as well as regression.', 'start': 33443.921, 'duration': 2.744}, {'end': 33450.051, 'text': 'Now, these are all are part of the ML live library here.', 'start': 33447.025, 'duration': 3.026}, {'end': 33453.296, 'text': "Let's see how we can perform these types of functions and queries.", 'start': 33450.371, 'duration': 2.925}, {'end': 33473.657, 'text': 'The first of all what we need to do is initialize the spark context.', 'start': 33470.555, 'duration': 3.102}], 'summary': 'Using a heart disease prediction model with decision tree for classification and regression in ml live library.', 'duration': 35.645, 'max_score': 33438.012, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY33438012.jpg'}], 'start': 31885.534, 'title': 'Pyspark and mllib', 'summary': 'Covers topics including data prediction, pyspark introduction, spark frameworks and transformations, data frames, and pyspark mllib overview, achieving 100% accuracy for some data predictions, introducing pyspark for real-time analytics and machine learning, demonstrating installation and operations of spark, and implementing a heart disease prediction model with a test error of 0.2297 for classification and a mean squared error of 0.168 for regression.', 'chapters': [{'end': 31927.514, 'start': 31885.534, 'title': 'Data prediction and accuracy analysis', 'summary': 'Demonstrates data set input from row 93 till 100, predicting classes with 100% accuracy for some values and 0% for others.', 'duration': 41.98, 'highlights': ['The chapter demonstrates data set input from row 93 till 100, predicting classes with 100% accuracy for some values and 0% for others.', 'The accuracy of the predictions is 100% for certain classes and 0% for others.', 'The input data set ranges from row 93 to 100, with the exclusion of the 101st row.']}, {'end': 32497.718, 'start': 31928.109, 'title': 'Introduction to pyspark', 'summary': 'Introduces pyspark, a powerful framework heavily used in the industry for real-time analytics and machine learning purposes, covering topics such as its ecosystem, advantages, installation process, and fundamental concepts, including spark context, rdd, and transformations.', 'duration': 569.609, 'highlights': ['PySpark is a powerful framework heavily used in the industry for real-time analytics and machine learning purposes. Apache Spark is being heavily used in the industry for real-time analytics and machine learning purposes.', 'PySpark provides various advantages such as ease of use, flexibility, resilience, and extensible APIs in different languages like Scala, Python, and Java. PySpark provides advantages in terms of ease of use, flexibility, resilience, and extensible APIs in different languages like Scala, Python, and Java.', 'The installation process for PySpark involves downloading the latest version of Spark release, ensuring compatibility with Hadoop, and setting up the necessary environment variables. The installation process for PySpark involves downloading the latest version of Spark release, ensuring compatibility with Hadoop, and setting up the necessary environment variables.', 'The Spark context is the heart of any Spark application, setting up internal services, establishing a connection to a Spark execution environment, and enabling the creation of RDDs, accumulators, and broadcast variables. The Spark context is the heart of any Spark application, enabling the creation of RDDs, accumulators, and broadcast variables.', 'RDDs (Resilient Distributed Datasets) are the building blocks of any Spark application, operating on multiple nodes for parallel processing on a cluster, and featuring fault tolerance and immutability. RDDs are the building blocks of any Spark application, featuring fault tolerance and immutability for parallel processing on a cluster.', 'Transformations and actions are key components of RDD operations, with transformations being lazy and actions triggering parallel computation to produce results. Transformations and actions are key components of RDD operations, with transformations being lazy and actions triggering parallel computation to produce results.']}, {'end': 32891.557, 'start': 32497.738, 'title': 'Spark frameworks and transformations', 'summary': 'Covers the installation of hadoop and spark, setting up jupyter notebook for spark, basic rdd operations, broadcast and accumulators in spark, spark configuration, and spark files.', 'duration': 393.819, 'highlights': ['The chapter covers the installation of Hadoop and Spark, setting up Jupyter notebook for Spark, basic RDD operations, broadcast and accumulators in Spark, Spark configuration, and spark files. It provides an overview of the various frameworks installed, such as Hadoop and Spark, and their setup, including using Jupyter notebook for Spark, basic RDD operations, broadcast and accumulators in Spark, Spark configuration, and spark files.', 'The moment we install and unzip it, I have shifted all my frameworks to one particular location. After installing and unzipping, all the frameworks are shifted to a specific location, ensuring organized management.', 'The sample data I have taken here is about blockchain. As you can see, we have one, two, three, four and five elements here. The sample data used for demonstration is related to blockchain, consisting of five elements.', "All I need to do is initialize another RDD, which is the num underscore RDD, and we use the sc.parallelize, and the range we have given is 1 to 10,000, and will use the reduce action here to see the output. Initialization of another RDD, 'num_RDD', using 'sc.parallelize' with a range of 1 to 10,000, followed by the use of the reduce action to obtain the output.", 'One of them is broadcast and the other one is the accumulator now broadcast variables are used to save the copy of data on all the notes in a cluster. Explanation of broadcast variables used to store data copies across all nodes in a cluster and the purpose of accumulators for aggregating information with associative and commutative operations.']}, {'end': 33294.686, 'start': 32891.557, 'title': 'Data frames in pyspark', 'summary': 'Introduces data frames in pyspark, a distributed collection of rows under named columns with common attributes with rdds, allowing lazy evaluation and designed for processing large structured or semi-structured data, and demonstrates creating, transforming, and querying data frames in pyspark using nyc flight data, including creating temporary tables and performing sql queries.', 'duration': 403.129, 'highlights': ['Data frames in PySpark are distributed collections of rows under named columns with common attributes with RDDs, allowing lazy evaluation, and designed for processing large structured or semi-structured data.', 'Creating a data frame in PySpark using NYC flight data, loading the data using the spark.read.CSV method, providing parameters such as info schema and header to avoid inferring the schema from the first row, and using the show action to display the top 20 rows of the data set.', 'Performing transformations and actions on the data frame such as printing the schema, using the count function to determine the number of records (3,036,776 records), selecting specific columns, and using the describe function to get a summary of a particular column.', 'Applying the filter function to filter data based on specific conditions such as distance, origin, and day of flight, using the filter function and the where clause for filtering, and using the and symbol to separate multiple conditions in the where clause.', 'Creating a temporary table for SQL queries using register.temp table to convert the data frame into a table, executing SQL queries on the created table, and performing nested SQL queries to retrieve specific data based on conditions.']}, {'end': 33763.902, 'start': 33295.127, 'title': 'Pyspark mllib overview', 'summary': 'Introduces the pyspark mllib library, highlighting its features such as storage levels, mllib machine learning api, and specific algorithms like collaborative filtering, clustering, frequent pattern matching, linear algebra, classification, and regression. a heart disease prediction model is implemented using mllib for classification and regression with a decision tree, achieving a test error of 0.2297 for classification and a mean squared error of 0.168 for regression.', 'duration': 468.775, 'highlights': ['A heart disease prediction model is implemented using MLlib for classification and regression with a decision tree, achieving a test error of 0.2297 for classification and a mean squared error of 0.168 for regression. The chapter covers the implementation of a heart disease prediction model using PySpark MLlib for classification and regression with a decision tree. It achieves a test error of 0.2297 for classification and a mean squared error of 0.168 for regression.', 'The PySpark MLlib library features storage levels, MLlib machine learning API, and specific algorithms like collaborative filtering, clustering, frequent pattern matching, linear algebra, classification, and regression. The PySpark MLlib library is highlighted, showcasing its features such as storage levels, MLlib machine learning API, and specific algorithms like collaborative filtering, clustering, frequent pattern matching, linear algebra, classification, and regression.']}], 'duration': 1878.368, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/-6RqxhNO2yY/pics/-6RqxhNO2yY31885534.jpg', 'highlights': ['The chapter covers the implementation of a heart disease prediction model using PySpark MLlib for classification and regression with a decision tree. It achieves a test error of 0.2297 for classification and a mean squared error of 0.168 for regression.', 'PySpark provides advantages in terms of ease of use, flexibility, resilience, and extensible APIs in different languages like Scala, Python, and Java.', 'RDDs are the building blocks of any Spark application, featuring fault tolerance and immutability for parallel processing on a cluster.', 'Creating a data frame in PySpark using NYC flight data, loading the data using the spark.read.CSV method, providing parameters such as info schema and header to avoid inferring the schema from the first row, and using the show action to display the top 20 rows of the data set.', 'The accuracy of the predictions is 100% for certain classes and 0% for others.']}], 'highlights': ["Python's out-of-the-box features have made it the top choice for data science, simplifying the process and contributing to its popularity.", 'The course is divided into six modules, offering a comprehensive understanding of data science using Python, covering topics such as environment setup, statistics, Python libraries, machine learning, deep learning, and PySpark.', 'Jupyter notebook is a modern-day tool for data scientists, similar to lab notebooks.', 'The session agenda covers a comprehensive overview of various topics related to statistics and probability, providing a structured plan for the session.', 'The outlook variable has the highest information gain value of 0.247, making it the best choice for the root node in the decision tree.', 'The Bayes Theorem is discussed, along with its mathematical representation and the meaning of key terms like likelihood ratio, posterior, and prior.', 'Numpy offers advantages over lists due to its memory efficiency, speed, and convenience, making it a preferred choice for data science applications.', 'Pandas is a software module for high-performance data manipulation and analysis.', 'The chapter introduces Python data analysis basics such as mean, median, mode, and variance, and demonstrates their calculation using Python, with an example sequence and its results.', 'Machine learning enables computers to learn without explicit programming, leading to improved sales and personalized care.', 'Logistic regression achieves 97% accuracy in cross-validation for digit classification, demonstrating its effectiveness in predicting digit values.', 'Deep learning is achieved through neural networks with multiple hidden layers, enabling complex problem-solving.', "The wine data set from Kaggle consists of 12 columns of data including country, description, designation, points, price, province, region one, region two, taster's name, Twitter handle, title, variety, and winery, providing detailed information about the wine.", 'The deep learning model is trained and its accuracy is observed to increase with every iteration, aiming to achieve the highest possible accuracy.', "Achieving a test accuracy of 85% The model achieves a test accuracy of 85%, indicating the accuracy of the model's predictions on the test data.", 'The chapter covers the implementation of a heart disease prediction model using PySpark MLlib for classification and regression with a decision tree. It achieves a test error of 0.2297 for classification and a mean squared error of 0.168 for regression.']}