title

Data Science Course | Data Science Tutorial | Intellipaat

description

ðŸ”¥Intellipaat Data Science course: https://intellipaat.com/data-science-architect-masters-program-training/
In this data science tutorial video you will know everything you need to become a data scientist. This data science tutorial covers topics such as what is data science, data scientists roles, their day to day tasks, data scientists salary, major statistics and probability concepts, data visualization, data manipulation, end to end data science project, data science interview questions and a lot more. If you are looking for a data science full course then this data science tutorial is just the right video for you.
#datasciencecourse #datasciencetutorial #datascience #datasciencecourses #DataSciencefullcourse #Intellipaat
ðŸ”¥ðŸ”¥ðŸ”¥Following topics are covered in this:
00:00 - Data Science Course
01:48 - What is Data Science Tutorial?
04:55 - Why do we need Data Science?
06:37 - Data Science Process
10:46 - Data Gathering
11:50 - Data Processing
12:35 - Data Analysis
13:23 - Data Cleaning
14:50 - Data Visualization
16:51 - Creating a Model
18:35 - Testing the Model
19:40 - Data Scientist Responsibilities
21:42 - Avg. Salary of a Data Scientist
22:23 - Data Science Tools
24:37 - Data Science Key Skills
31:08 - Data Science Learning Path
38:25 - Statistics and Probability
01:41:14 - What is Probability?
01:52:22 - Three Approaches to Probability
01:57:00 - Bayesian Theorem
02:11:10 - Contingency Table
02:33:55 - Independent Event
02:36:15 - Bayesian Tree
02:51:22 - Quiz
02:53:38 - Sample Distribution
03:24:49 - Systematic Sampling
03:54:47 - Poisson Distributions
04:00:55 - Why do we need Algorithms?
04:02:51 - What are Algorithms?
04:06:15 - Introduction to Machine Learning
04:07:30 - Types of Machine Learning
04:08:04 - Supervised Learning
04:12:26 - Logistic Regression
04:15:11 - Unsupervised Learning
04:17:35 - Reinforcement Learning
04:30:32 - What is Numpy?
04:45:07 - Rabdamize an Array
05:08:06 - Indexing and Slicing in Python
05:34:58 - Advantages of Numpy over list
05:44:13 - Introduction to Pandas
06:51:55 - Quiz
06:54:39 - Introduction to Matplotlib
07:00:24 - Types of Plots
08:18:50 - Data Science Project
09:00:00 - Data Science Interview Questions
ðŸ“Œ Do subscribe to Intellipaat channel & get regular updates on videos: http://bit.ly/Intellipaat
ðŸ“• Read complete Data Science tutorial here: https://intellipaat.com/blog/tutorial/data-science-tutorial/
ðŸ”— Watch Data Science tutorials here:- https://bit.ly/30QlOmv
ðŸ“• Read insightful blog on what is Data Science: https://intellipaat.com/blog/what-is-data-science/
ðŸ“°Interested to know about Data Science certifications? Read this blog: https://intellipaat.com/blog/data-science-certification/
Are you looking for something more? Enroll in our Data Science course and become a certified Data Scientist (https://intellipaat.com/data-science-architect-masters-program-training/). It is a 232 hrs instructor led Data Science training provided by Intellipaat which is completely aligned with industry standards and certification bodies.
Why Data Science is important?
Data Science is taking over each and every industry domain. Machine Learning and especially Deep Learning are the most important aspects of Data Science that are being deployed everywhere from search engines to online movie recommendations. Taking the Intellipaat Data Science training & Data Science Course can help professionals to build a solid career in a rising technology domain and get the best jobs in top organizations.
Why should you opt for a Data Science career?
If you want to fast-track your career then you should strongly consider Data Science. The reason for this is that it is one of the fastest growing technology. There is a huge demand for Data Scientist. The salaries for Data Scientist is fantastic.There is a huge growth opportunity in this domain as well. Hence this Intellipaat Data Science tutorial is your stepping stone to a successful career!
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Data science involves finding hidden patterns from raw and structured data, where 80% of the data gathered by any company is likely to be unstructured.', 'The importance of data science lies in its ability to extract meaningful information out of unstructured data, which was previously difficult to parse and extract information from. Data science is important for extracting meaningful information from unstructured data, which was previously difficult to parse and extract information from.', 'The use cases of data science include social media analytics, predictive analysis, targeted ads, augmented reality, recommendation engines, and healthcare imaging. Use cases of data science encompass social media analytics, predictive analysis, targeted ads, augmented reality, recommendation engines, and healthcare imaging.', 'The data science process involves understanding the business problem, data gathering, data analysis, processing data, data visualization, data cleaning, and creating a model. The data science process includes understanding the business problem, data gathering, data analysis, processing data, data visualization, data cleaning, and creating a model.']}], 'duration': 573.542, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo3049.jpg', 'highlights': ['The demand for data scientists has increased due to the abundance of information and the lack of resources to analyze it, leading to a huge demand for data scientists.', 'Data science is the process of finding hidden patterns from the raw and structured data, with unstructured data accounting for 80% of data gathered by companies.', 'The session covers the basic concepts, machine learning algorithms like linear regression, logistic regression, decision tree, random forest, and data visualization using Matplotlib.', 'Statistics and probability are highlighted as essential prerequisites for becoming a data scientist, emphasizing their importance in the field.', 'The use cases of data science include social media analytics, predictive analysis, targeted ads, augmented reality, recommendation engines, and healthcare imaging.', 'The data science process involves understanding the business problem, data gathering, data analysis, processing data, data visualization, data cleaning, and creating a model.', 'The importance of data science lies in its ability to extract meaningful information out of unstructured data, which was previously difficult to parse and extract information from.']}, {'end': 1872.001, 'segs': [{'end': 719.412, 'src': 'embed', 'start': 691.269, 'weight': 0, 'content': [{'end': 697.586, 'text': 'however, we have gotten the wrong kind of data and that data is not useful for us to solve the problem,', 'start': 691.269, 'duration': 6.317}, {'end': 702.288, 'text': "and we've essentially wasted a lot of time in gathering data that we do not need.", 'start': 697.586, 'duration': 4.702}, {'end': 710.231, 'text': 'so when we move on to the next step, we need to understand that we have gathered the right kind of data, which is why the first step was so important.', 'start': 702.288, 'duration': 7.943}, {'end': 711.472, 'text': 'then comes data processing.', 'start': 710.231, 'duration': 1.241}, {'end': 719.412, 'text': "so when we're trying to process some data, what we're trying to do is we're trying to convert data into easily readable formats.", 'start': 712.668, 'duration': 6.744}], 'summary': 'Importance of gathering the right data for problem-solving and the need for data processing to convert data into readable formats.', 'duration': 28.143, 'max_score': 691.269, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo691269.jpg'}, {'end': 808.442, 'src': 'embed', 'start': 783.173, 'weight': 4, 'content': [{'end': 802.963, 'text': 'We can also take a look at the standard variance or the standard deviation of our data set to understand just how different our data is from each other and what are the things that we can do to make sure that our data and process does not get bulked up in later stages because of some problem in our data.', 'start': 783.173, 'duration': 19.79}, {'end': 805.685, 'text': 'Then we move on to data cleaning.', 'start': 804.084, 'duration': 1.601}, {'end': 808.442, 'text': 'Now, data cleaning is also very important.', 'start': 806.26, 'duration': 2.182}], 'summary': 'Analyzing variance and data cleaning are crucial for ensuring data quality and process efficiency.', 'duration': 25.269, 'max_score': 783.173, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo783173.jpg'}, {'end': 1044.599, 'src': 'embed', 'start': 1001.463, 'weight': 5, 'content': [{'end': 1005.705, 'text': 'So data visualization is mostly used to understand the trend of the data.', 'start': 1001.463, 'duration': 4.242}, {'end': 1007.966, 'text': "This is basically a story that we're trying to tell.", 'start': 1006.005, 'duration': 1.961}, {'end': 1010.447, 'text': 'And this is why we use data visualization.', 'start': 1008.446, 'duration': 2.001}, {'end': 1013.168, 'text': 'Then comes creating a model.', 'start': 1011.967, 'duration': 1.201}, {'end': 1015.853, 'text': "So, when we're trying to create a model,", 'start': 1013.953, 'duration': 1.9}, {'end': 1023.695, 'text': "what we're trying to do is we're trying to create a mathematical representation of the information that we have extracted from the data set.", 'start': 1015.853, 'duration': 7.842}, {'end': 1030.757, 'text': 'This mathematical information could be reused to perform some tasks like classification,', 'start': 1024.095, 'duration': 6.662}, {'end': 1035.838, 'text': 'or maybe make some prediction about what the future sales are going to be like.', 'start': 1030.757, 'duration': 5.081}, {'end': 1040.079, 'text': "future stocks are going to be like, depending on the data and depending on the things that we're trying to do.", 'start': 1035.838, 'duration': 4.241}, {'end': 1044.599, 'text': 'So as you can see, we get a lot of raw input data.', 'start': 1041.617, 'duration': 2.982}], 'summary': 'Data visualization crucial for understanding trends and making predictions from raw input data.', 'duration': 43.136, 'max_score': 1001.463, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo1001463.jpg'}, {'end': 1210.477, 'src': 'embed', 'start': 1185.888, 'weight': 10, 'content': [{'end': 1195.131, 'text': 'So data scientist has many responsibilities, but some of the most common ones are analytics, strategy, and collaboration.', 'start': 1185.888, 'duration': 9.243}, {'end': 1197.132, 'text': "So let's take a look at them one by one.", 'start': 1195.892, 'duration': 1.24}, {'end': 1204.535, 'text': 'So when it comes to analytics, a data scientist must be able to analyze the data and extract some useful information out of it.', 'start': 1197.572, 'duration': 6.963}, {'end': 1207.956, 'text': "That's the basic thing that a data scientist must be able to do.", 'start': 1204.555, 'duration': 3.401}, {'end': 1210.477, 'text': "That's the core part of their job.", 'start': 1208.116, 'duration': 2.361}], 'summary': 'Data scientists must excel in analytics to extract useful information from data.', 'duration': 24.589, 'max_score': 1185.888, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo1185888.jpg'}, {'end': 1301.43, 'src': 'embed', 'start': 1274.676, 'weight': 11, 'content': [{'end': 1282.38, 'text': "So, for instance, if you're trying to build a heart disease prediction or a heart disease detection system,", 'start': 1274.676, 'duration': 7.704}, {'end': 1285.622, 'text': 'a data scientist must be able to work with a doctor, a cardiologist,', 'start': 1282.38, 'duration': 3.242}, {'end': 1292.285, 'text': 'a radiologist and any concerned person and just understand what are the things that lead to a heart disease.', 'start': 1285.622, 'duration': 6.663}, {'end': 1296.047, 'text': 'What is the data that you need to access and how you can get that,', 'start': 1292.685, 'duration': 3.362}, {'end': 1301.43, 'text': 'and then work with them to understand if the data is correct and just take them through the entire data science process.', 'start': 1296.047, 'duration': 5.383}], 'summary': 'Data scientists collaborate with healthcare professionals to develop heart disease prediction systems by understanding the disease factors and accessing and verifying the necessary data.', 'duration': 26.754, 'max_score': 1274.676, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo1274676.jpg'}, {'end': 1406.936, 'src': 'embed', 'start': 1363.059, 'weight': 1, 'content': [{'end': 1368.964, 'text': "They complete some of the code that you're going to write and they help you avoid errors and make mistakes.", 'start': 1363.059, 'duration': 5.905}, {'end': 1370.906, 'text': 'So they save you a lot of time.', 'start': 1369.424, 'duration': 1.482}, {'end': 1379.052, 'text': "And having a good grasp of the IDE of the programming language that you're using to create your own data science pipeline is very important.", 'start': 1371.406, 'duration': 7.646}, {'end': 1384.388, 'text': "So if you're using Python, the IDE that you could use is Anaconda.", 'start': 1379.747, 'duration': 4.641}, {'end': 1389.21, 'text': 'There are many other ideas for Python as well, like PyCharm, so on and so forth.', 'start': 1385.009, 'duration': 4.201}, {'end': 1391.691, 'text': "And if you're using Java, there's Eclipse.", 'start': 1389.91, 'duration': 1.781}, {'end': 1394.471, 'text': "If you're using R, then RStudio is probably the best.", 'start': 1391.871, 'duration': 2.6}, {'end': 1400.073, 'text': 'But depending on your taste, you could try out multiple IDEs and figure out which one suits you the best.', 'start': 1394.971, 'duration': 5.102}, {'end': 1402.973, 'text': 'Then comes data visualization tools.', 'start': 1401.192, 'duration': 1.781}, {'end': 1406.936, 'text': 'These tools can be used quite sparsely or quite heavily,', 'start': 1403.374, 'duration': 3.562}], 'summary': 'Ides like anaconda save time. multiple ides for python, eclipse for java, rstudio for r. data visualization tools vary in usage.', 'duration': 43.877, 'max_score': 1363.059, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo1363059.jpg'}], 'start': 576.591, 'title': 'Data science fundamentals', 'summary': 'Provides insights into problem understanding, data science process overview, introduction to data science, and essential skills for data science. it emphasizes the significance of understanding the problem, data gathering, processing, and essential skills in data science, and provides insights into the average salary of data scientists in the us and india.', 'chapters': [{'end': 612.013, 'start': 576.591, 'title': 'Problem understanding process', 'summary': 'Discusses the importance of understanding the problem, asking why it needs to be solved, and determining the end product, emphasizing the significance of these steps in the problem-solving process.', 'duration': 35.422, 'highlights': ['Understanding the problem is the first and the most important part of this process.', 'Asking why the problem needs to be solved and what benefit it provides is crucial to the problem-solving process.', 'Determining the end product and the problems that might be faced while solving contribute to the overall understanding of the problem.']}, {'end': 976.454, 'start': 612.173, 'title': 'Data science process overview', 'summary': 'Explains the importance of data gathering, processing, analysis, cleaning, visualization, and correlation in the data science process, emphasizing the significance of gathering the right kind of data and the impact of data cleaning on the accuracy of analysis.', 'duration': 364.281, 'highlights': ['Importance of Data Gathering Emphasizes the significance of gathering the right kind of data for effective problem-solving, stressing that gathering the wrong kind of data can lead to wasted time and ineffective problem-solving.', 'Data Cleaning Importance Highlights the significance of data cleaning in removing inaccurate or unwanted data, providing examples of inaccurate data and methods for handling missing values.', 'Data Visualization Benefits Explains the benefits of data visualization, such as understanding trends and patterns, and the ability to view changes over time seamlessly, along with understanding the correlation between variables.']}, {'end': 1487.028, 'start': 977.314, 'title': 'Introduction to data science', 'summary': 'Discusses the process of data visualization, creating a model, testing the model, responsibilities of a data scientist, and essential tools and skills. it also provides insights into the average salary of data scientists in the us and india.', 'duration': 509.714, 'highlights': ['The process of data visualization is used to understand the trend of the data and tell a story through user-friendly formats. Data visualization helps in understanding data trends and conveying a story effectively.', 'Creating a model involves creating a mathematical representation of the information extracted from the data set, which can be used for tasks like classification and future sales prediction. Creating a model includes developing a mathematical representation for tasks such as classification and predicting future sales.', 'Testing a model involves evaluating the accuracy score by checking the correctness of predictions made on testing data, using terminologies like confusion matrix and accuracy score. Testing a model includes assessing accuracy through metrics like confusion matrix and accuracy score.', 'Responsibilities of a data scientist include analytics, strategy, and collaboration, where they analyze data, use information to guide business strategy, and collaborate with experts from various domains to solve complex problems. Data scientists are responsible for analytics, strategy formulation, and collaborating with domain experts to solve complex problems.', "The average salary for a junior data scientist in the US is around $140,000, while a senior data scientist earns about $185,000. In India, a junior data scientist's average salary is approximately 15.7 lakhs per annum, and a senior data scientist's average salary is about 21.5 lakhs per annum. The average salaries for junior and senior data scientists in the US and India are provided for comparison.", 'Key tools for data scientists include integrated development environments (IDEs) like Anaconda for Python, data visualization tools such as Tableau, data processing tools like Apache Spark, and data analytics tools like SAS and RapidMiner. Essential tools for data scientists include IDEs, data visualization, data processing, and data analytics tools.', 'Mathematics is emphasized as a crucial skill for data scientists. Mathematics is highlighted as a critical skill for data scientists.']}, {'end': 1872.001, 'start': 1487.468, 'title': 'Essential skills for data science', 'summary': "Emphasizes the essential skills required for data science, including statistics, probability, calculus, databases, programming, and data analytics, highlighting the importance of foundational knowledge in these areas in advancing one's career.", 'duration': 384.533, 'highlights': ["The importance of foundational knowledge in statistics, probability, and calculus in advancing one's career in data science Understanding the basic concepts of statistics, probability, and calculus can significantly help in advancing one's career in data science.", 'The significance of databases in data science and the need to understand different types of databases and how they work together Understanding how to use different types of databases and make them work together is crucial in data science.', 'The core competency of programming in data science and the flexibility it provides in defining the data science process Programming gives great flexibility in defining the data science process, allowing for automation of tasks and interaction with data visualization and analytics tools.', 'The importance of foundational knowledge in programming languages such as Python and R for data science Python and R are the most popular programming languages for data science, providing expressive and clear syntax and widely used in business, academic, and research environments.', 'The significance of data analytics in extracting key insights from large datasets and making informed business decisions Data analytics allows the extraction of key insights from large datasets, enabling informed business decisions and avoiding incorrect results or responses.']}], 'duration': 1295.41, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo576591.jpg', 'highlights': ['The average salary for a senior data scientist in the US is about $185,000.', 'The average salary for a junior data scientist in the US is around $140,000.', 'The average salary for a senior data scientist in India is about 21.5 lakhs per annum.', 'The average salary for a junior data scientist in India is approximately 15.7 lakhs per annum.', 'Understanding the problem is the first and the most important part of this process.', 'Asking why the problem needs to be solved and what benefit it provides is crucial to the problem-solving process.', 'Determining the end product and the problems that might be faced while solving contribute to the overall understanding of the problem.', 'Importance of Data Gathering Emphasizes the significance of gathering the right kind of data for effective problem-solving.', 'Data Cleaning Importance Highlights the significance of data cleaning in removing inaccurate or unwanted data.', 'Data Visualization Benefits Explains the benefits of data visualization, such as understanding trends and patterns.', 'Creating a model involves creating a mathematical representation of the information extracted from the data set.', 'Testing a model involves evaluating the accuracy score by checking the correctness of predictions made on testing data.', 'Responsibilities of a data scientist include analytics, strategy, and collaboration.', 'Key tools for data scientists include integrated development environments (IDEs) like Anaconda for Python.', 'Mathematics is emphasized as a crucial skill for data scientists.', "The importance of foundational knowledge in statistics, probability, and calculus in advancing one's career in data science.", 'The significance of databases in data science and the need to understand different types of databases and how they work together.', 'The core competency of programming in data science and the flexibility it provides in defining the data science process.', 'The importance of foundational knowledge in programming languages such as Python and R for data science.', 'The significance of data analytics in extracting key insights from large datasets and making informed business decisions.']}, {'end': 4096.462, 'segs': [{'end': 2156.399, 'src': 'embed', 'start': 2125.176, 'weight': 2, 'content': [{'end': 2127.078, 'text': 'But this has been highly successful.', 'start': 2125.176, 'duration': 1.902}, {'end': 2133.023, 'text': 'Many, many real world projects or real world products use them.', 'start': 2127.198, 'duration': 5.825}, {'end': 2139.69, 'text': 'Things like Google Lens also uses it to take a look at a photo and extract some text out of it.', 'start': 2133.826, 'duration': 5.864}, {'end': 2142.272, 'text': 'And many other applications of it are available.', 'start': 2140.13, 'duration': 2.142}, {'end': 2149.357, 'text': 'Similarly, things like the facial recognition, facial classification, if classification and recognition are two different things.', 'start': 2142.672, 'duration': 6.685}, {'end': 2156.399, 'text': 'Recognition basically means taking a look at a photo and understanding if that photo contains one specific person.', 'start': 2150.254, 'duration': 6.145}], 'summary': 'Highly successful in real world projects; used in google lens for text extraction and facial recognition.', 'duration': 31.223, 'max_score': 2125.176, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo2125176.jpg'}, {'end': 2288.338, 'src': 'embed', 'start': 2262.601, 'weight': 1, 'content': [{'end': 2267.764, 'text': 'And once you understand that, then you can move ahead and understand how to do the things that you want to do.', 'start': 2262.601, 'duration': 5.163}, {'end': 2274.707, 'text': 'And after you get certified, you get a competitive edge and your chances of getting shortlisted improve greatly.', 'start': 2268.284, 'duration': 6.423}, {'end': 2277.612, 'text': 'And then comes apply for the jobs.', 'start': 2276.271, 'duration': 1.341}, {'end': 2282.455, 'text': 'After you have performed these steps, you are well on your way to becoming a data scientist.', 'start': 2277.832, 'duration': 4.623}, {'end': 2288.338, 'text': 'Depending on the experience that you have and the skills that you have, you could get really good paying salaries.', 'start': 2283.015, 'duration': 5.323}], 'summary': 'Certification improves job prospects, leading to high-paying data scientist roles.', 'duration': 25.737, 'max_score': 2262.601, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo2262601.jpg'}, {'end': 2727.305, 'src': 'embed', 'start': 2697.131, 'weight': 0, 'content': [{'end': 2703.014, 'text': 'now I will have to give some kind of summary of this data.', 'start': 2697.131, 'duration': 5.883}, {'end': 2712.159, 'text': 'so whenever you are trying to give the summary of this data, then, if you have to, then the particular field is called descriptive statistics.', 'start': 2703.014, 'duration': 9.145}, {'end': 2718.182, 'text': 'okay, as I mentioned earlier, descriptive strategy is the term given to the analysis of the data.', 'start': 2712.159, 'duration': 6.023}, {'end': 2727.305, 'text': 'so you are going to do the analysis of the data and here you are not going to do any projections or any predictions, any estimations.', 'start': 2718.182, 'duration': 9.123}], 'summary': 'Descriptive statistics analyze data, no projections or predictions.', 'duration': 30.174, 'max_score': 2697.131, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo2697131.jpg'}, {'end': 3675.249, 'src': 'embed', 'start': 3646.078, 'weight': 4, 'content': [{'end': 3648.159, 'text': 'but from the business point of view,', 'start': 3646.078, 'duration': 2.081}, {'end': 3661.044, 'text': 'let us say you are hired by one big e-commerce site where you are going to give some kind of suggestions to the management to take decisions.', 'start': 3648.159, 'duration': 12.885}, {'end': 3663.585, 'text': 'okay, in terms of revenue growth.', 'start': 3661.044, 'duration': 2.541}, {'end': 3667.366, 'text': 'let us take this particular example.', 'start': 3665.425, 'duration': 1.941}, {'end': 3672.348, 'text': 'okay, look at this particular page and there are four products.', 'start': 3667.366, 'duration': 4.982}, {'end': 3675.249, 'text': 'okay, Samsung Galaxy S3 Neo.', 'start': 3672.348, 'duration': 2.901}], 'summary': 'Provide revenue growth suggestions for an e-commerce site with samsung galaxy s3 neo.', 'duration': 29.171, 'max_score': 3646.078, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo3646078.jpg'}], 'start': 1872.884, 'title': 'Data science essentials', 'summary': 'Covers essential skills, including programming in python and r, portfolio building, obtaining certifications, and applying for data science jobs. it also emphasizes the importance of understanding descriptive and inferential statistics for data analysis and interpretation.', 'chapters': [{'end': 1951.905, 'start': 1872.884, 'title': 'Data science learning path', 'summary': 'Discusses the path to becoming a data scientist, even with no prior experience, emphasizing the importance of understanding mathematics, statistics, probability, calculus, and linear algebra to make informed decisions based on gathered insights.', 'duration': 79.021, 'highlights': ['Understanding mathematics, statistics, probability, calculus, and linear algebra is crucial for becoming a data scientist, even for complete beginners.', 'Statistics is used to ensure that the data represents the real world and is crucial for making informed decisions based on insights gathered.', 'Calculus is used to ensure that the model is performing correctly, emphasizing the importance of mathematical knowledge in data science.', 'The learning path emphasizes the combination of programming knowledge with acquired mathematical skills to make informed decisions based on insights gathered.']}, {'end': 2351.823, 'start': 1951.905, 'title': 'Data science essentials', 'summary': 'Covers the essential skills and steps required to become a data scientist, including the use of programming languages such as python and r, building a portfolio with projects, obtaining certifications, and applying for data science jobs.', 'duration': 399.918, 'highlights': ['The most popular languages for data science tasks are Python and R, with good support for machine learning. Python and R are the most popular languages for data science tasks, especially in business and academic environments, with good support for machine learning.', 'Building a portfolio with small projects and solving real-world problems using data science skills is crucial for gaining competitive edge and increasing chances of getting shortlisted for data science jobs. Building a portfolio with small projects and solving real-world problems using data science skills is crucial for gaining a competitive edge and increasing chances of getting shortlisted for data science jobs.', "Obtaining certifications is important for data scientists to showcase their skills and increase their chances of getting hired. Obtaining certifications is important for data scientists to showcase their skills and increase their chances of getting hired, as it allows potential employers to evaluate the candidate's skills and expertise.", 'Applying for data science jobs after following the necessary steps can lead to well-paying positions and opportunities to solve real-world problems. Applying for data science jobs after following the necessary steps can lead to well-paying positions and opportunities to solve real-world problems, using the acquired data science skills and expertise.']}, {'end': 2798.787, 'start': 2351.823, 'title': 'Descriptive statistics and data analysis', 'summary': 'Discusses the importance of descriptive statistics in analyzing and summarizing data, using examples such as batsman performance, engineering graduates, stock prices, and population projections, emphasizing the need for raw data analysis and noise removal.', 'duration': 446.964, 'highlights': ["Descriptive statistics are used to analyze and summarize data, such as a batsman's performance, engineering graduates, stock prices, and population projections, highlighting the importance of understanding raw data and removing noise.", 'An example of using descriptive statistics is analyzing the ratio of chemical engineering graduates within 10 years, emphasizing the significance of tracking changes and trends in the data.', 'The chapter emphasizes the importance of understanding raw data and conducting noise removal before performing any analysis, highlighting the necessity for data preprocessing to eliminate unnecessary information.', 'The speaker mentions the importance of giving a meaningful summary of data, exemplified by the population production of Delhi, and explains the role of descriptive statistics in providing insights without predictions or estimations.']}, {'end': 3221.224, 'start': 2798.787, 'title': 'Descriptive statistics in data interpretation', 'summary': 'Discusses the concept of descriptive statistics in data interpretation, including the population data population projection of delhi from 2002 to 2016, and the process of understanding, finding useful information, interpreting, and visualizing the data.', 'duration': 422.437, 'highlights': ['Descriptive statistics enables us to present the data in a more meaningful way. It allows for a simple interpretation of data, making it more meaningful and easy to understand.', 'Explanation of population data population projection of Delhi from 2002 to 2016. The data explained the population projection of Delhi from 2002 to 2016, providing a specific focus on the population data.', 'Process of understanding, finding useful information, interpreting, and visualizing the data. The chapter emphasizes the process of understanding data, finding useful information, interpreting patterns, and visualizing the data for effective data analysis.']}, {'end': 4096.462, 'start': 3221.224, 'title': 'Descriptive vs inferential statistics', 'summary': 'Covers the difference between descriptive and inferential statistics, emphasizing the importance of using statistical measures to interpret and draw conclusions from raw data, and provides an example of summarizing and analyzing sales data for different products over a period of time.', 'duration': 875.238, 'highlights': ['Descriptive statistics involves presenting and describing sample data, using measures such as average, percentage of growth, and maximum and minimum values to interpret data, providing a collection, presentation, and description of the data. Descriptive statistics involves presenting and describing sample data, using measures such as average, percentage of growth, and maximum and minimum values to interpret data, providing a collection, presentation, and description of the data.', 'Inferential statistics entails making decisions and drawing conclusions from the data, such as analyzing population growth rates and demographics to make decisions relevant to specific segments, and involves calculating measures like velocity and median to understand growth patterns. Inferential statistics entails making decisions and drawing conclusions from the data, such as analyzing population growth rates and demographics to make decisions relevant to specific segments, and involves calculating measures like velocity and median to understand growth patterns.', 'Using descriptive statistics to summarize sales data for different products over a period, including measures such as minimum, maximum, sum, mean, and median to understand the sales patterns over time. Using descriptive statistics to summarize sales data for different products over a period, including measures such as minimum, maximum, sum, mean, and median to understand the sales patterns over time.']}], 'duration': 2223.578, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo1872884.jpg', 'highlights': ['Understanding mathematics, statistics, probability, calculus, and linear algebra is crucial for becoming a data scientist, even for complete beginners.', 'Statistics is used to ensure that the data represents the real world and is crucial for making informed decisions based on insights gathered.', 'The most popular languages for data science tasks are Python and R, with good support for machine learning.', 'Building a portfolio with small projects and solving real-world problems using data science skills is crucial for gaining a competitive edge and increasing chances of getting shortlisted for data science jobs.', 'Obtaining certifications is important for data scientists to showcase their skills and increase their chances of getting hired.', "Descriptive statistics are used to analyze and summarize data, such as a batsman's performance, engineering graduates, stock prices, and population projections, highlighting the importance of understanding raw data and removing noise.", 'Inferential statistics entails making decisions and drawing conclusions from the data, such as analyzing population growth rates and demographics to make decisions relevant to specific segments, and involves calculating measures like velocity and median to understand growth patterns.', 'The learning path emphasizes the combination of programming knowledge with acquired mathematical skills to make informed decisions based on insights gathered.']}, {'end': 5772.871, 'segs': [{'end': 4986.62, 'src': 'embed', 'start': 4950.171, 'weight': 2, 'content': [{'end': 4955.573, 'text': 'where we were trying to compare the salaries of for two people coming from san francisco, new york.', 'start': 4950.171, 'duration': 5.402}, {'end': 4968.259, 'text': "why can't we do the same thing for a very uh, not very big uh, for a data set that is reasonably better, okay,", 'start': 4955.573, 'duration': 12.686}, {'end': 4971.68, 'text': "so i'm not talking about any very big data table.", 'start': 4968.259, 'duration': 3.421}, {'end': 4986.62, 'text': 'let us talk about a small data table that is, having total 100 customers who had come to my website and had purchased different products,', 'start': 4971.68, 'duration': 14.94}], 'summary': "Comparing salaries of two people from san francisco and new york, aiming for a better dataset with 100 customers' purchase data.", 'duration': 36.449, 'max_score': 4950.171, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo4950171.jpg'}, {'end': 5033.663, 'src': 'embed', 'start': 5001.367, 'weight': 0, 'content': [{'end': 5013.134, 'text': 'price and what is the emi value that it is actually showing there in that in that website, and number of sellers okay.', 'start': 5001.367, 'duration': 11.767}, {'end': 5020.679, 'text': 'so it is also the feature of the product who are all selling, and ram size, whether it is having dual sim or not.', 'start': 5013.134, 'duration': 7.545}, {'end': 5022.88, 'text': 'and what is the resolution of the camera.', 'start': 5020.679, 'duration': 2.201}, {'end': 5026.181, 'text': 'so now, similar data set.', 'start': 5023.54, 'duration': 2.641}, {'end': 5033.663, 'text': 'assume that you have collected for for your experimental study data, okay,', 'start': 5026.181, 'duration': 7.482}], 'summary': 'Transcript discusses price, emi value, number of sellers, product features, ram size, dual sim, and camera resolution.', 'duration': 32.296, 'max_score': 5001.367, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo5001367.jpg'}, {'end': 5093.837, 'src': 'embed', 'start': 5062.987, 'weight': 3, 'content': [{'end': 5068.71, 'text': 'so there, if i am actually going to do that, the first thing, summary of the data.', 'start': 5062.987, 'duration': 5.723}, {'end': 5073.153, 'text': 'i would say that number of rows is 100, okay.', 'start': 5068.71, 'duration': 4.443}, {'end': 5074.173, 'text': 'and number of columns?', 'start': 5073.153, 'duration': 1.02}, {'end': 5077.74, 'text': 'how many columns do I have?', 'start': 5075.598, 'duration': 2.142}, {'end': 5086.83, 'text': '11. now I want to calculate them mean max, mean median standard deviation and number of unique values.', 'start': 5077.74, 'duration': 9.09}, {'end': 5093.837, 'text': 'so here I just want to explain you one more concept, that is the type of variables.', 'start': 5086.83, 'duration': 7.007}], 'summary': 'Data summary: 100 rows, 11 columns, mean, max, median, std dev, unique values', 'duration': 30.85, 'max_score': 5062.987, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo5062987.jpg'}, {'end': 5154.104, 'src': 'embed', 'start': 5126.267, 'weight': 1, 'content': [{'end': 5135.313, 'text': 'okay, there can be situations that the, the, the values will be numerical but may not having some value.', 'start': 5126.267, 'duration': 9.046}, {'end': 5140.536, 'text': 'so, in order to explain this particular, before i go and summarize this data,', 'start': 5135.313, 'duration': 5.223}, {'end': 5150.682, 'text': 'it is very important for us to understand what are the various types of variables that we generally encounter in our data.', 'start': 5140.536, 'duration': 10.146}, {'end': 5154.104, 'text': 'okay, so you might say that, hey, by looking at this one.', 'start': 5150.682, 'duration': 3.422}], 'summary': 'Explaining the types of variables encountered in data analysis.', 'duration': 27.837, 'max_score': 5126.267, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo5126267.jpg'}], 'start': 4096.462, 'title': 'Understanding variable profiles and data analysis', 'summary': 'Covers the concept of variable profiles, emphasizing the importance of characteristics such as min, max, mean, median, and standard deviation, and discusses the role of mean, median, mad, variance, and standard deviation in data analysis, including examples and comparisons between different variables and data types.', 'chapters': [{'end': 4223.494, 'start': 4096.462, 'title': 'Understanding variable profiles', 'summary': 'Discusses the concept of variable profiles, emphasizing the importance of understanding the characteristics such as min, max, mean, median, and standard deviation, and provides an example of sales profile consisting of minimum sales of 220, maximum sales of 542, total sales of 1753, average sales of 350.6, and median of 311.', 'duration': 127.032, 'highlights': ['The chapter emphasizes the importance of understanding the characteristics of variable profiles, including min, max, mean, median, and standard deviation. Understanding the characteristics of variable profiles, such as min, max, mean, median, and standard deviation, is crucial in comprehending the distribution of data.', 'An example of a sales profile is provided, consisting of minimum sales of 220, maximum sales of 542, total sales of 1753, average sales of 350.6, and median of 311. An example of a sales profile illustrates the concept, with specific data points including minimum sales of 220, maximum sales of 542, total sales of 1753, average sales of 350.6, and median of 311.', 'The significance of these values is highlighted, as they collectively form the profile of a variable, providing a comprehensive understanding of its distribution. The values of min, max, mean, median, and standard deviation collectively form the profile of a variable, offering a comprehensive insight into its distribution.']}, {'end': 4520.897, 'start': 4223.494, 'title': 'Importance of mean and median in data analysis', 'summary': 'Discusses the importance of mean and median in characterizing the distribution of variables, highlighting their role in identifying skewness and the need for additional measures to evaluate characteristics such as range and dispersion, illustrated through a comparison of salaries in san francisco and new york.', 'duration': 297.403, 'highlights': ['The distribution of variables can be easily characterized by mean and median, aiding in identifying skewness in the distribution.', 'Comparison of salaries in San Francisco and New York reveals the need for additional measures to evaluate characteristics such as range and dispersion, despite the mean and median appearing the same.', 'Range, calculated as maximum minus minimum, is a measure of variability and can be used to assess the differences in distributions of salaries between two companies.']}, {'end': 5181.634, 'start': 4520.897, 'title': 'Descriptive statistics for salary variability', 'summary': "Explores the concept of mean absolute deviation (mad), variance, standard deviation, and coefficient of variation to analyze salary variability between employees in san francisco and new york, where the mean absolute deviation for san francisco is 15,000 and for new york is 25,000, indicating lower variability in san francisco. the chapter also discusses coefficient of skewness and provides an example of applying descriptive statistics to a small data table of 100 customers' information.", 'duration': 660.737, 'highlights': ['The mean absolute deviation for San Francisco is 15,000 and for New York is 25,000, indicating lower variability in San Francisco. The mean absolute deviation (MAD) is 15,000 for San Francisco and 25,000 for New York, suggesting lower salary variability in San Francisco.', 'The standard deviation is 17,078 for San Francisco and 25,000 for New York, reflecting the variability in salaries from the mean. The standard deviation is 17,078 for San Francisco and 25,000 for New York, indicating the extent of salary variability from the mean.', "The coefficient of variation is 57% for San Francisco and 83% for New York, demonstrating less variation in San Francisco's salaries compared to those in New York. The coefficient of variation is 57% for San Francisco and 83% for New York, indicating lower salary variation in San Francisco.", "The chapter provides an example of applying descriptive statistics to a small data table of 100 customers' information, aiming to summarize the data using mean, max, median, standard deviation, and number of unique values. The chapter discusses applying descriptive statistics to a small data table of 100 customers' information to summarize the data using mean, max, median, standard deviation, and number of unique values."]}, {'end': 5772.871, 'start': 5181.634, 'title': 'Types of variables and data analysis', 'summary': 'Discusses the concepts of quantitative and qualitative variables, discrete and continuous variables, and nominal and ordinal variables, emphasizing the importance of building a hierarchy of variables for data analysis and applying specific descriptive statistics methods based on variable types.', 'duration': 591.237, 'highlights': ['The chapter discusses the concepts of quantitative and qualitative variables, discrete and continuous variables, and nominal and ordinal variables. It explains the differences between quantitative and qualitative variables, and also delves into discrete and continuous variables, as well as nominal and ordinal variables.', 'Emphasizes the importance of building a hierarchy of variables for data analysis. It stresses the significance of creating a hierarchy of variables to gain a clear understanding of the data and its variables.', 'Applying specific descriptive statistics methods based on variable types. It mentions the importance of applying specific descriptive statistics methods tailored to the types of variables, such as mean and median for quantitative variables, and counting unique values for qualitative variables.']}], 'duration': 1676.409, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo4096462.jpg', 'highlights': ['The significance of min, max, mean, median, and standard deviation in understanding variable profiles.', 'Comparison of salaries in San Francisco and New York reveals the need for additional measures.', 'The importance of building a hierarchy of variables for data analysis.', 'Applying specific descriptive statistics methods based on variable types.', 'An example of a sales profile illustrates the concept of variable profiles.']}, {'end': 6880.924, 'segs': [{'end': 6253.765, 'src': 'embed', 'start': 6224.588, 'weight': 1, 'content': [{'end': 6232.275, 'text': 'unlikely means not very impossible, but yeah, chances are very less like that.', 'start': 6224.588, 'duration': 7.687}, {'end': 6235.017, 'text': 'okay, even chance, even chance means, which is the 50% probability.', 'start': 6232.275, 'duration': 2.742}, {'end': 6243.295, 'text': 'okay, if if you, if any of your friend asks you okay, are you coming to the party tomorrow?', 'start': 6236.468, 'duration': 6.827}, {'end': 6253.765, 'text': 'you say that, yeah, it is 50, 50, because there are many kind of constraints you have in your mind that you may have to go and attend some conference,', 'start': 6243.295, 'duration': 10.47}], 'summary': 'Unlikely means chances are very less, even chance is 50% probability.', 'duration': 29.177, 'max_score': 6224.588, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo6224588.jpg'}, {'end': 6379.296, 'src': 'embed', 'start': 6345.758, 'weight': 6, 'content': [{'end': 6347.019, 'text': 'There are subjective probability.', 'start': 6345.758, 'duration': 1.261}, {'end': 6349.201, 'text': "It is a person's belief.", 'start': 6347.98, 'duration': 1.221}, {'end': 6354.845, 'text': 'Empirical probability, which is nothing but he takes the data and talks about it.', 'start': 6349.861, 'duration': 4.984}, {'end': 6356.526, 'text': 'And then theoretical probability.', 'start': 6355.345, 'duration': 1.181}, {'end': 6362.909, 'text': 'theoretical probability is mostly like i mentioned, right, what are the high school standards?', 'start': 6357.126, 'duration': 5.783}, {'end': 6365.23, 'text': 'we learned all these theoretical probabilities.', 'start': 6362.909, 'duration': 2.321}, {'end': 6366.13, 'text': 'okay, which is nothing.', 'start': 6365.23, 'duration': 0.9}, {'end': 6374.193, 'text': 'but actually you just say that how many ways that it can occur, which is something like how many, how many, how many,', 'start': 6366.13, 'duration': 8.063}, {'end': 6379.296, 'text': 'how many satellite launches will be successful?', 'start': 6374.193, 'duration': 5.103}], 'summary': 'Transcript discusses subjective, empirical, and theoretical probability with high school standards and examples.', 'duration': 33.538, 'max_score': 6345.758, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo6345758.jpg'}, {'end': 6785.319, 'src': 'embed', 'start': 6717.296, 'weight': 2, 'content': [{'end': 6719.077, 'text': 'only one second time.', 'start': 6717.296, 'duration': 1.781}, {'end': 6722.06, 'text': 'okay, then the probability will come out to be very less.', 'start': 6719.077, 'duration': 2.983}, {'end': 6725.923, 'text': 'in the similar, in the similar fashion, whenever you are solving any problem,', 'start': 6722.06, 'duration': 3.863}, {'end': 6730.306, 'text': 'you should completely understand the complete sample space and you have to define your own events first.', 'start': 6725.923, 'duration': 4.383}, {'end': 6759.047, 'text': 'and my belief that hillary clinton wins these elections is different from your belief here.', 'start': 6730.306, 'duration': 28.741}, {'end': 6768.83, 'text': 'i and you are not using any data, but we are using our previous experiences, our readings from the newspapers, news channels and all these.', 'start': 6759.047, 'duration': 9.783}, {'end': 6778.552, 'text': 'based on that, i am coming with some belief that this is the value, that particular probability that hillary clinton would be.', 'start': 6768.83, 'duration': 9.722}, {'end': 6782.098, 'text': 'It varies from person to person.', 'start': 6779.437, 'duration': 2.661}, {'end': 6785.319, 'text': 'okay?. I would say that there is a high probability that Messi would come back.', 'start': 6782.098, 'duration': 3.221}], 'summary': 'Understanding sample space is crucial for defining events and determining probabilities.', 'duration': 68.023, 'max_score': 6717.296, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo6717296.jpg'}, {'end': 6855.734, 'src': 'embed', 'start': 6834.437, 'weight': 0, 'content': [{'end': 6844.86, 'text': "okay. so as we go down, i'm going to put, i'm going to teach you that how we can introduce this subject to probability values in solving your problems.", 'start': 6834.437, 'duration': 10.423}, {'end': 6846.681, 'text': 'and second is empirical probability.', 'start': 6844.86, 'duration': 1.821}, {'end': 6853.143, 'text': 'empirical probability is nothing, but you conduct the experiments and then you calculate the problem.', 'start': 6846.681, 'duration': 6.462}, {'end': 6855.734, 'text': 'You conduct the experiments.', 'start': 6854.554, 'duration': 1.18}], 'summary': 'Introducing probability values and empirical probability in problem solving.', 'duration': 21.297, 'max_score': 6834.437, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo6834437.jpg'}], 'start': 5772.871, 'title': 'Probability concepts and data visualization', 'summary': 'Covers the importance of data summary and visualization, including the relevance of data dictionary in statistical studies, and explains the concepts of likely, unlikely, and even chance probabilities, along with subjective, empirical, and theoretical probabilities, and the significance of sample space and events in calculating probabilities. it also discusses the concept of probability in problem-solving, emphasizing the need to define sample space and events, and highlights subjective and empirical probabilities, with examples of practical applications and decision-making.', 'chapters': [{'end': 6224.588, 'start': 5772.871, 'title': 'Data summary and visualization', 'summary': 'Covers the importance of data summary and visualization, including the relevance of data dictionary in statistical studies and its applications in making informed decisions in machine learning models and customer behavior analysis.', 'duration': 451.717, 'highlights': ['The relevance of data dictionary in statistical studies and its applications in making informed decisions in machine learning models and customer behavior analysis. The chapter emphasizes the importance of a data dictionary in statistical studies and its role in making informed decisions in machine learning models and analyzing customer behavior.', 'The significance of data summary and visualization in providing more understandable information compared to numerical values. The importance of data summary and visualization is highlighted as they provide more understandable information compared to numerical values, aiding in decision-making processes.', 'The challenge of providing certain answers to questions related to customer behavior, inventory cost, airfare, market conditions, and share prices due to uncertainty. The difficulty in providing certain answers to questions related to customer behavior, inventory cost, airfare, market conditions, and share prices due to uncertainty is discussed.', 'Explanation of probability as a gradation from 0 to 1, with 0 representing impossible events and 1 representing events with high certainty. The concept of probability is explained as a gradation from 0 to 1, with 0 representing impossible events and 1 representing events with high certainty.']}, {'end': 6513.654, 'start': 6224.588, 'title': 'Types of probabilities and events', 'summary': 'Explains the concepts of likely, unlikely, and even chance probabilities, along with subjective, empirical, and theoretical probabilities, and the significance of sample space and events in calculating probabilities.', 'duration': 289.066, 'highlights': ['The chapter covers likely, unlikely, and even chance probabilities, along with subjective, empirical, and theoretical probabilities. It explains the different types of probabilities, such as likely, unlikely, and even chance, as well as subjective, empirical, and theoretical probabilities.', 'The significance of sample space and events in calculating probabilities is emphasized. It emphasizes the importance of sample space, which comprises all possible combinations, and events, which are the specific outcomes of interest in calculating probabilities.', "The concept of subjective probability based on personal belief is discussed. It discusses subjective probability, which is based on an individual's belief or perception."]}, {'end': 6880.924, 'start': 6513.654, 'title': 'Understanding probability in problem solving', 'summary': 'Discusses the concept of probability in problem-solving, emphasizing the need to define sample space and events, and highlights subjective and empirical probabilities, with examples of practical applications and decision-making.', 'duration': 367.27, 'highlights': ['The need to define sample space and events is emphasized when solving probability problems in industry, to better understand the problem and formulate it effectively.', 'Subjective probabilities are discussed, stating that personal beliefs and experiences influence individual probability assessments, impacting decision-making in scenarios such as project acceptance and career advancement.', "The concept of empirical probability is introduced, highlighting the importance of conducting experiments and gathering data to calculate probabilities, illustrated with the example of cricket player Sachin's century-making probabilities against Australia."]}], 'duration': 1108.053, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo5772871.jpg', 'highlights': ['The relevance of data dictionary in statistical studies and its applications in making informed decisions in machine learning models and customer behavior analysis.', 'The significance of data summary and visualization in providing more understandable information compared to numerical values.', 'The challenge of providing certain answers to questions related to customer behavior, inventory cost, airfare, market conditions, and share prices due to uncertainty.', 'The concept of probability as a gradation from 0 to 1, with 0 representing impossible events and 1 representing events with high certainty.', 'The chapter covers likely, unlikely, and even chance probabilities, along with subjective, empirical, and theoretical probabilities.', 'The significance of sample space and events in calculating probabilities is emphasized.', 'The need to define sample space and events is emphasized when solving probability problems in industry, to better understand the problem and formulate it effectively.', 'Subjective probabilities are discussed, stating that personal beliefs and experiences influence individual probability assessments, impacting decision-making in scenarios such as project acceptance and career advancement.', "The concept of empirical probability is introduced, highlighting the importance of conducting experiments and gathering data to calculate probabilities, illustrated with the example of cricket player Sachin's century-making probabilities against Australia."]}, {'end': 8591, 'segs': [{'end': 7010.599, 'src': 'embed', 'start': 6979.349, 'weight': 4, 'content': [{'end': 6985.873, 'text': 'they would say that this is something like going to happen, but that particular era is going to be changed,', 'start': 6979.349, 'duration': 6.524}, {'end': 6996.159, 'text': 'with the data is arriving into our servers okay, like, which means that we are getting more data, that is, we are having more empirical results,', 'start': 6985.873, 'duration': 10.286}, {'end': 6998.761, 'text': 'the real values we are having.', 'start': 6996.159, 'duration': 2.602}, {'end': 7010.599, 'text': 'so Industries are actually using both SMEs and empirical probabilities and then getting the most powerful insights in terms of business decisions.', 'start': 6998.761, 'duration': 11.838}], 'summary': 'More data is changing the industry, leading to powerful insights for business decisions.', 'duration': 31.25, 'max_score': 6979.349, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo6979349.jpg'}, {'end': 7697.4, 'src': 'embed', 'start': 7635.103, 'weight': 0, 'content': [{'end': 7638.965, 'text': "In this contingency table, we'll learn all the conditional probabilities.", 'start': 7635.103, 'duration': 3.862}, {'end': 7641.687, 'text': "I'll give you the complete details of it.", 'start': 7639.466, 'duration': 2.221}, {'end': 7654.055, 'text': 'And then also we have to understand that product and addition rules, how exactly can we use these rules, and also we will cover here.', 'start': 7642.368, 'duration': 11.687}, {'end': 7669.762, 'text': 'So most of you know that product rule can be used trying to, which is something like students and coming from New York.', 'start': 7654.455, 'duration': 15.307}, {'end': 7671.563, 'text': 'okay, both the events occur.', 'start': 7669.762, 'duration': 1.801}, {'end': 7680.386, 'text': 'so your question tells that I want to know the sales for the students and they are coming from New York.', 'start': 7671.563, 'duration': 8.823}, {'end': 7686.228, 'text': 'okay. another question is that I want to find out the sales for students.', 'start': 7680.386, 'duration': 5.842}, {'end': 7690.449, 'text': 'okay, or coming from New York, something like.', 'start': 7686.228, 'duration': 4.221}, {'end': 7692.499, 'text': 'So that is addition rule W.', 'start': 7690.799, 'duration': 1.7}, {'end': 7697.4, 'text': 'So try to understand the difference and or based on that you can choose probabilities.', 'start': 7692.499, 'duration': 4.901}], 'summary': 'Learn conditional probabilities, product and addition rules, and their applications in sales.', 'duration': 62.297, 'max_score': 7635.103, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo7635103.jpg'}, {'end': 8006.642, 'src': 'embed', 'start': 7976.246, 'weight': 5, 'content': [{'end': 7977.807, 'text': 'first level information i had.', 'start': 7976.246, 'duration': 1.561}, {'end': 7986.738, 'text': 'now, given the condition that virat kohli is playing or is not playing, that gives me another level of information.', 'start': 7977.807, 'duration': 8.931}, {'end': 7990.078, 'text': 'in order to know that one, in order to know that one,', 'start': 7986.738, 'duration': 3.34}, {'end': 8000.04, 'text': 'you should know what is the probability of what is the probability of virat kohli that he is actually making good runs or some information?', 'start': 7990.078, 'duration': 9.962}, {'end': 8002.061, 'text': 'you have to know about it, right.', 'start': 8000.04, 'duration': 2.021}, {'end': 8006.642, 'text': 'so here i wanted to explain the condition probability very easily.', 'start': 8002.061, 'duration': 4.581}], 'summary': "Explaining the concept of conditional probability using the example of virat kohli's performance.", 'duration': 30.396, 'max_score': 7976.246, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo7976246.jpg'}, {'end': 8368.58, 'src': 'embed', 'start': 8338.799, 'weight': 2, 'content': [{'end': 8344.317, 'text': 'correct, this is the total and this is the total.', 'start': 8338.799, 'duration': 5.518}, {'end': 8349.001, 'text': 'okay, the overall total is 94.', 'start': 8344.317, 'duration': 4.684}, {'end': 8350.522, 'text': 'so, in this continuous table,', 'start': 8349.001, 'duration': 1.521}, {'end': 8357.772, 'text': "what I'm going to do is that I want to find out what is the probability of people coming from main to who are from New York.", 'start': 8350.522, 'duration': 7.25}, {'end': 8363.696, 'text': 'That is nothing but the total candidates I have 94, which is nothing but 20 by 94.', 'start': 8358.092, 'duration': 5.604}, {'end': 8364.816, 'text': 'This is that value.', 'start': 8363.696, 'duration': 1.12}, {'end': 8367.539, 'text': 'Okay C3 by this.', 'start': 8365.437, 'duration': 2.102}, {'end': 8368.58, 'text': 'This is that value.', 'start': 8367.799, 'duration': 0.781}], 'summary': 'The overall total is 94, with a probability of 20 out of 94 people coming from new york.', 'duration': 29.781, 'max_score': 8338.799, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo8338799.jpg'}], 'start': 6882.475, 'title': 'Probability and multivariable analysis', 'summary': 'Covers practical applications of probability, including theoretical, subjective, and empirical probability, k-way tables for multivariable analysis, bayesian statistics, prior and conditional probabilities, and contingency table analysis, providing tools for decision-making and insights in predictive analysis and machine learning.', 'chapters': [{'end': 7418.962, 'start': 6882.475, 'title': 'Probability in practical applications', 'summary': 'Discusses the practical application of probability, including theoretical, subjective, and empirical probability, as well as the use of bayesian theorem in industry problems, where subject matter experts and empirical probabilities are employed for business decisions.', 'duration': 536.487, 'highlights': ['Theoretical probability is discussed as the calculation of probability without data or subject matter experts, while subject matter experts and empirical probabilities are utilized in industry for powerful business insights. Discussion on theoretical probability, subject matter experts, and empirical probabilities in industry for business insights.', 'The use of Bayesian theorem in practical applications and the importance of understanding the relationships among variables in solving problems are emphasized. Emphasis on the use of Bayesian theorem and understanding relationships among variables in practical applications.', 'Explanation of the elementary level of probability involving common examples like determining the probability of getting a certain number when throwing a dice. Discussion on elementary level probability and common examples.', 'The importance of understanding the relationships among variables in practical applications, such as the impact of product pricing on sales, is highlighted. Emphasis on understanding relationships among variables in practical applications, using product pricing and sales as an example.', 'The explanation of contingency tables for determining the relationship between two category variables is provided. Explanation of contingency tables for determining the relationship between two category variables.']}, {'end': 7588.83, 'start': 7418.962, 'title': 'K-way tables for multivariable analysis', 'summary': 'Introduces the concept of k-way tables for analyzing multiple variables, such as gender, city, price, and employment type, to understand their impact on sales and patterns in data, paving the way for insights and decision-making in predictive analysis and machine learning.', 'duration': 169.868, 'highlights': ['K-way tables are used for analyzing multiple variables, such as gender, city, price, and employment type, to understand their impact on sales and patterns in data. This highlights the core concept of the chapter, emphasizing the practical application of k-way tables in analyzing various variables and their impact on sales and data patterns.', 'The ability to analyze and understand the relationships between variables is crucial for making informed decisions in predictive analysis and machine learning. This is important as it underscores the significance of understanding variable relationships for informed decision-making in predictive analysis and machine learning.', 'Visualizing high-dimensional contingency tables, such as six-dimensional or even thousand-dimensional tables, is challenging, leading to the development of algorithms to handle and find relationships within such tables. This highlight demonstrates the difficulty in visualizing high-dimensional tables and the need for algorithms to handle and analyze relationships within these tables.']}, {'end': 7795.339, 'start': 7588.83, 'title': 'Bayesian statistics & contingency tables', 'summary': 'Discusses the use of contingency tables in understanding conditional probabilities and applying the bayesian rules, emphasizing the difference from frequentist approach and providing a fundamental understanding of bayesian statistics.', 'duration': 206.509, 'highlights': ['The chapter explains the use of contingency tables in understanding conditional probabilities. It covers how to calculate the conditional probabilities and the product and addition rules.', 'It emphasizes the difference between Bayesian statistics and frequentist approach. The chapter prompts the audience to understand the distinction between the two approaches and their implications.', 'Provides a fundamental understanding of Bayesian statistics. It introduces the concept of Bayesian statistics and relates it to the human understanding of the world.']}, {'end': 8166.04, 'start': 7795.339, 'title': 'Understanding prior probabilities and conditional probabilities', 'summary': 'Explains the concept of prior probabilities and conditional probabilities, using examples to demonstrate how prior information impacts the probability of an event, and how conditional probabilities are calculated based on given conditions and independent variables.', 'duration': 370.701, 'highlights': ['The chapter explains the concept of prior probabilities and how prior information impacts the probability of an event. The speaker discusses how prior information about Narendra Modi implementing a policy in the Indian economy influences the probability of certain events, illustrating the impact of prior information on probability.', 'The chapter provides examples to demonstrate how conditional probabilities are calculated based on given conditions and independent variables. The speaker uses examples of cricket matches and car phone usage to explain conditional probabilities, highlighting the calculation process based on given conditions and independent variables.', 'The chapter introduces the concept of contingency tables and their role in determining conditional probabilities. The speaker explains that contingency tables provide a different way of calculating probabilities and help in determining conditional probabilities quite easily, thus highlighting their significance in probability calculations.']}, {'end': 8368.58, 'start': 8166.04, 'title': 'Probability and contingency table analysis', 'summary': 'Discusses the use of contingency tables and joint probabilities to analyze the relationship between variables, with a focus on the example of phone use in cars and speeding violations, and emphasizes the calculation of joint probabilities and understanding of dependency and independence.', 'duration': 202.54, 'highlights': ['The chapter emphasizes the calculation of joint probabilities and understanding of dependency and independence, providing practical examples to illustrate the concepts. The chapter provides practical examples and emphasizes the calculation of joint probabilities, aiming to help understand dependency and independence.', 'The discussion involves the analysis of the relationship between phone use in cars and speeding violations using a contingency table. The chapter focuses on analyzing the relationship between phone use in cars and speeding violations using a contingency table as an example.', 'The example of phone use in cars and speeding violations is used to demonstrate the application of contingency tables in analyzing real-world scenarios. A real-world example of phone use in cars and speeding violations is used to demonstrate the application of contingency tables in analyzing real-world scenarios.']}, {'end': 8591, 'start': 8369.34, 'title': 'Understanding joint and conditional probability', 'summary': 'Explains the concept of joint probability, emphasizing that the complete joint probability of all values equals 1. it further illustrates the calculation of specific joint probabilities for different scenarios and introduces the concept of conditional probability.', 'duration': 221.66, 'highlights': ['Joint probability of people coming from Chicago and being males is 40 out of 94', 'Probability of people coming from New York is represented by a specific value in the joint probability', 'Explanation of the joint probability of people coming from Chicago and being females as a specific value', 'Introduction of the concept of conditional probability given specific conditions']}], 'duration': 1708.525, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo6882475.jpg', 'highlights': ['Emphasis on the use of Bayesian theorem and understanding relationships among variables in practical applications.', 'This highlights the core concept of the chapter, emphasizing the practical application of k-way tables in analyzing various variables and their impact on sales and data patterns.', 'The ability to analyze and understand the relationships between variables is crucial for making informed decisions in predictive analysis and machine learning.', 'The chapter explains the use of contingency tables in understanding conditional probabilities.', 'The chapter emphasizes the calculation of joint probabilities and understanding of dependency and independence, providing practical examples to illustrate the concepts.', 'The chapter explains the concept of prior probabilities and how prior information impacts the probability of an event.']}, {'end': 11094.323, 'segs': [{'end': 9428.075, 'src': 'embed', 'start': 9358.714, 'weight': 0, 'content': [{'end': 9367.531, 'text': 'I am just making up all these examples, but my point is that here, whenever you are trying to find out the relationship between the events, so you,', 'start': 9358.714, 'duration': 8.817}, {'end': 9375.198, 'text': 'you generally apply these conditions, and if the problem is coming out to be the same, then those are not dependent.', 'start': 9367.531, 'duration': 7.667}, {'end': 9390.462, 'text': 'well, we are going to cover the Bayesian relationship between the probability of relationship between,', 'start': 9375.198, 'duration': 15.264}, {'end': 9400.01, 'text': 'if it is not equal to it is something like relationship between probability of a given the condition B okay,', 'start': 9390.462, 'duration': 9.548}, {'end': 9405.475, 'text': 'and probability of B given to the condition a, so which is nothing.', 'start': 9400.01, 'duration': 5.465}, {'end': 9406.355, 'text': 'but let me write down.', 'start': 9405.475, 'duration': 0.88}, {'end': 9423.993, 'text': 'See this Bayesian theorem is going to use in Bayesian networks, okay, that is, actually interact with lots of many variables.', 'start': 9414.949, 'duration': 9.044}, {'end': 9428.075, 'text': 'Let me just figure it out.', 'start': 9424.433, 'duration': 3.642}], 'summary': 'Discusses bayesian relationship between probabilities in finding event relationships in bayesian networks.', 'duration': 69.361, 'max_score': 9358.714, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo9358714.jpg'}, {'end': 9638.55, 'src': 'embed', 'start': 9607.066, 'weight': 1, 'content': [{'end': 9608.949, 'text': 'fail. or why is girls?', 'start': 9607.066, 'duration': 1.883}, {'end': 9611.471, 'text': 'are accidents not accidents?', 'start': 9608.949, 'duration': 2.522}, {'end': 9613.173, 'text': 'market plan payments?', 'start': 9611.471, 'duration': 1.702}, {'end': 9614.675, 'text': "any event i'm taking okay.", 'start': 9613.173, 'duration': 1.502}, {'end': 9621.641, 'text': "so now what I'm going to explain here is that this is the probability of event s probability of event.", 'start': 9615.478, 'duration': 6.163}, {'end': 9625.864, 'text': 'no, okay, that is what F and F dash here.', 'start': 9621.641, 'duration': 4.223}, {'end': 9632.087, 'text': 'this event is okay occurring, given the condition that this is happening.', 'start': 9625.864, 'duration': 6.223}, {'end': 9635.149, 'text': 'here this event is not occurring.', 'start': 9632.087, 'duration': 3.062}, {'end': 9638.55, 'text': 'that is not occurring given the condition yep.', 'start': 9635.149, 'duration': 3.401}], 'summary': 'Discussion on probability of events and conditional occurrences.', 'duration': 31.484, 'max_score': 9607.066, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo9607066.jpg'}, {'end': 11004.169, 'src': 'embed', 'start': 10979.482, 'weight': 4, 'content': [{'end': 10987.544, 'text': 'Without replacement, so that we can minimize the person who had already come, already tested again.', 'start': 10979.482, 'duration': 8.062}, {'end': 10992.086, 'text': 'come and attend the same test, which is something like 60 students.', 'start': 10987.544, 'duration': 4.542}, {'end': 10997.087, 'text': 'I have tested one, then I would just keep him aside and I will go to the next population.', 'start': 10992.086, 'duration': 5.001}, {'end': 10998.908, 'text': 'My population has reduced to 59.', 'start': 10997.127, 'duration': 1.781}, {'end': 11001.469, 'text': 'I will test it.', 'start': 10998.908, 'duration': 2.561}, {'end': 11003.369, 'text': '58, I will test it.', 'start': 11001.489, 'duration': 1.88}, {'end': 11004.169, 'text': '57, I will test it.', 'start': 11003.389, 'duration': 0.78}], 'summary': 'Testing 60 students with decreasing population for retesting.', 'duration': 24.687, 'max_score': 10979.482, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo10979482.jpg'}], 'start': 8591, 'title': 'Bayesian rules and conditional probability', 'summary': 'Explains the concept of conditional probability and bayesian rules, with examples and calculations, emphasizing the impact of different teachers on student pass/fail probabilities and the significance of sampling distribution in statistical studies.', 'chapters': [{'end': 8913.741, 'start': 8591, 'title': 'Bayesian rules for conditional probability', 'summary': 'Explains the concept of conditional probability using the example of calculating the probability of males given they are from new york, based on given data of male and female population, and the application of bayesian rules in the calculation.', 'duration': 322.741, 'highlights': ['The chapter discusses the concept of conditional probability, specifically focusing on calculating the probability of males given they are from New York, illustrating the application of Bayesian rules in the calculation.', 'The speaker emphasizes the application of conditional probability in focusing only on the specific condition, neglecting the rest of the population, exemplifying it with the distribution of people coming from New York and the calculation of the probability of males given they are from New York.', 'The transcript provides a detailed explanation of the application of conditional probability, showcasing the process of applying a condition to calculate the probability of a specific group within a given population.']}, {'end': 10300.753, 'start': 8913.741, 'title': 'Conditional probability and bayesian theorem', 'summary': 'Explains conditional probability, bayesian theorem, and independent events along with examples and calculations, emphasizing the relationship between events and probabilities.', 'duration': 1387.012, 'highlights': ['The chapter explains conditional probability and its application in finding the probability of gender given the city, emphasizing the distribution of male and female in New York and Chicago. Explanation of conditional probability and its application in finding the distribution of male and female in New York and Chicago.', 'The explanation of marginal probabilities and their application in various scenarios such as mortgage loan, credit card reports, and other problem-solving situations. Detailed explanation of marginal probabilities and their application in diverse scenarios.', 'The concept of independent events is discussed, emphasizing the absence of a relationship between events and the application of chi-square test. Discussion on independent events and the application of chi-square test.', 'The chapter delves into the Bayesian theorem and its utilization in Bayesian networks, emphasizing the relationship between different variables. Explanation of the Bayesian theorem and its utilization in Bayesian networks.', 'The explanation of calculating conditional probabilities using contingency tables and the application of the chain rule in finding joint probabilities. Detailed explanation of calculating conditional probabilities and the application of the chain rule in finding joint probabilities.']}, {'end': 10549.851, 'start': 10300.753, 'title': "Teacher's impact on student pass/fail", 'summary': 'Discusses the impact of different teachers on student pass/fail probabilities, the calculation of probability values, and the importance of sampling distribution in statistical studies.', 'duration': 249.098, 'highlights': ['The chapter focuses on calculating the probability of a student passing the class and understanding the impact of different teachers on student pass/fail probabilities. The discussion revolves around calculating the probability of a student passing the class and how different teachers influence the pass/fail probabilities of students.', 'The importance of sampling distribution in statistical studies is emphasized, particularly in scenarios where conducting studies on the entire population is impractical. The significance of sampling distribution is highlighted in the context of conducting statistical studies on large datasets, where studying the entire population is impractical due to time and resource constraints.', 'The discussion delves into the nature of the population and emphasizes the need to take samples based on the specific study requirements and the nature of the population. The chapter emphasizes the importance of taking samples based on the nature of the study and the specific characteristics of the population, particularly in scenarios with closed boundaries such as agricultural studies.']}, {'end': 11094.323, 'start': 10549.851, 'title': 'Sampling and significance testing', 'summary': 'Covers the concepts of sampling in bounded and unbounded populations, the significance of conducting studies on samples, and the distinction between sampling with replacement and without replacement, emphasizing their impact on statistical modeling.', 'duration': 544.472, 'highlights': ['The importance of determining whether a population is bounded or unbounded when conducting a study, as it dictates the approach to sampling and statistical analysis. Understanding the fixed or unbounded nature of the population is crucial in determining the sampling approach, impacting the subsequent statistical analysis.', 'The significance of conducting studies on samples rather than the complete population, illustrated through examples such as the random testing of Maggi packets and its impact on decision-making. The example of random testing of Maggi packets demonstrates the practical significance of conducting studies on samples, influencing decision-making processes.', 'The distinction between sampling with replacement and without replacement, and their respective implications for statistical modeling and experimental studies. Explaining the differences between sampling with replacement and without replacement, and their relevance in different scenarios such as statistical modeling and experimental studies.']}], 'duration': 2503.323, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo8591000.jpg', 'highlights': ['The chapter discusses the concept of conditional probability, focusing on calculating the probability of males given they are from New York.', 'The chapter focuses on calculating the probability of a student passing the class and understanding the impact of different teachers on student pass/fail probabilities.', 'The importance of determining whether a population is bounded or unbounded when conducting a study, as it dictates the approach to sampling and statistical analysis.', 'The significance of conducting studies on samples rather than the complete population, illustrated through examples such as the random testing of Maggi packets and its impact on decision-making.', 'The importance of sampling distribution in statistical studies is emphasized, particularly in scenarios where conducting studies on the entire population is impractical.']}, {'end': 13247.577, 'segs': [{'end': 11497.053, 'src': 'embed', 'start': 11465.922, 'weight': 3, 'content': [{'end': 11473.287, 'text': 'Then they will ask you the problem, can we predict? what kind of fraud that might happen in the future.', 'start': 11465.922, 'duration': 7.365}, {'end': 11482.45, 'text': 'Where exactly the fraud can happen? Can we predict? So you can predict at least with some 30% probability or 60% probability.', 'start': 11475.248, 'duration': 7.202}, {'end': 11487.051, 'text': 'You cannot predict, of course, with 100% probability fraud would happen on this day at this particular time.', 'start': 11482.77, 'duration': 4.281}, {'end': 11497.053, 'text': 'But by using the kind of transactions, the kind of timing of the transactions, the amount of the transactions and the velocity of the transactions,', 'start': 11487.864, 'duration': 9.189}], 'summary': 'Using transaction data to predict fraud with 30-60% probability.', 'duration': 31.131, 'max_score': 11465.922, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo11465922.jpg'}, {'end': 11553.312, 'src': 'embed', 'start': 11525.379, 'weight': 1, 'content': [{'end': 11533.751, 'text': 'So the distribution of that particular one, if you draw it, which is very big like this, and the other transactions are very less.', 'start': 11525.379, 'duration': 8.372}, {'end': 11546.326, 'text': 'Now you are going to build one artificial neural network model on all those variables, because artificial neural network or any other decision trees,', 'start': 11537.599, 'duration': 8.727}, {'end': 11553.312, 'text': 'some other machine learning models are actually proved to be finding this broad patterns most efficiently.', 'start': 11546.326, 'duration': 6.986}], 'summary': 'Building an artificial neural network model on various variables for efficient pattern finding.', 'duration': 27.933, 'max_score': 11525.379, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo11525379.jpg'}, {'end': 12541.692, 'src': 'embed', 'start': 12504.533, 'weight': 0, 'content': [{'end': 12506.054, 'text': 'If it is zero, then it is overlapping.', 'start': 12504.533, 'duration': 1.521}, {'end': 12513.57, 'text': 'Any two points, if you take in the Euclidean space, the distance between them is zero means they are overlapping.', 'start': 12507.384, 'duration': 6.186}, {'end': 12521.579, 'text': 'So they are not overlapping but at a distance of 0.1, 0.01, 0.8, 0.2, like that.', 'start': 12515.072, 'duration': 6.507}, {'end': 12527.465, 'text': 'Then it is something like representing as they are very close to each other but not the same.', 'start': 12522.119, 'duration': 5.346}, {'end': 12531.499, 'text': 'That is what this clustering.', 'start': 12529.356, 'duration': 2.143}, {'end': 12541.692, 'text': 'This defines a cluster where if everybody is matching, everybody is actually same, all together same, then it will be a point.', 'start': 12531.519, 'duration': 10.173}], 'summary': 'Clustering defines clusters based on closeness, not overlapping at a distance of 0.1, 0.01, 0.8, 0.2, etc.', 'duration': 37.159, 'max_score': 12504.533, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo12504533.jpg'}, {'end': 12742.556, 'src': 'embed', 'start': 12691.798, 'weight': 4, 'content': [{'end': 12701.004, 'text': 'So, I will just take only two, no four dimension I cannot take because we cannot visualize that and I am actually just taking three one.', 'start': 12691.798, 'duration': 9.206}, {'end': 12704.087, 'text': 'This is coming out to be something like.', 'start': 12701.765, 'duration': 2.322}, {'end': 12715.636, 'text': 'this is oceans.', 'start': 12711.915, 'duration': 3.721}, {'end': 12723.258, 'text': 'this is something like age.', 'start': 12715.636, 'duration': 7.622}, {'end': 12730.34, 'text': 'this is something like salary.', 'start': 12723.258, 'duration': 7.082}, {'end': 12742.556, 'text': "this is something like I'm taking only these three variables for our easy understanding.", 'start': 12730.34, 'duration': 12.216}], 'summary': 'Using three variables for easy understanding in a data visualization context.', 'duration': 50.758, 'max_score': 12691.798, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo12691798.jpg'}], 'start': 11094.363, 'title': 'Student selection and sampling techniques', 'summary': 'Covers student selection process for 800 seats from 5,000 students, emphasizes balanced regional representation; discusses distribution criteria, fraud analysis, and efficient sampling for ml with focus on rare events, confidence intervals, and various sampling techniques; and explains clustering and application of student t distribution for small sample sizes.', 'chapters': [{'end': 11249.646, 'start': 11094.363, 'title': 'Student selection criteria in institutes', 'summary': 'Discusses the process of selecting students for an institute, considering a scenario of 5,000 total students and 800 available seats, and emphasizes the need to maintain a balanced representation from different regions to ensure the same distribution percentage while selecting.', 'duration': 155.283, 'highlights': ['The chapter discusses the process of selecting students for an institute, considering a scenario of 5,000 total students and 800 available seats. It explains the process of selecting students based on a scenario of 5,000 total students and 800 available seats.', 'Emphasizes the need to maintain a balanced representation from different regions to ensure the same distribution percentage while selecting. It emphasizes the importance of creating different state bins to ensure a balanced representation from different regions and maintain the same distribution percentage.']}, {'end': 11524.549, 'start': 11249.666, 'title': 'Distribution criteria and fraud analysis', 'summary': 'Discusses the criteria for distribution, the concept of uniform distribution, and the analysis of fraud cases in atm transactions, with a focus on predicting future fraud occurrences based on historical data and transaction variables.', 'duration': 274.883, 'highlights': ['The chapter explains the criteria for distribution and the concept of uniform distribution, emphasizing the importance of selecting the population for statistical study based on the number of people and distribution strategy. Importance of selecting the population for statistical study, criteria for distribution, concept of uniform distribution', 'The speaker presents a real case of analyzing ATM transactions, discussing the occurrence of fraud cases, the monetary impact of the fraud, and the potential for predicting future fraud based on transaction variables and historical data. Analysis of ATM transaction fraud cases, monetary impact of fraud, potential for predicting future fraud']}, {'end': 12074.369, 'start': 11525.379, 'title': 'Efficient sampling for machine learning', 'summary': 'Discusses the importance of stratified sampling in building artificial neural network models on large datasets, emphasizing the need to ensure efficient representation of rare events and the role of confidence intervals in estimating population parameters.', 'duration': 548.99, 'highlights': ['The importance of stratified sampling in building artificial neural network models on large datasets Highlights the significance of using stratified sampling to efficiently represent rare events in large datasets for building accurate machine learning models.', 'Emphasizing the need to ensure efficient representation of rare events Stresses the importance of ensuring that rare events, such as fraudulent transactions, are effectively represented in the sampled data to build reliable models.', 'The role of confidence intervals in estimating population parameters Explains the role of confidence intervals in estimating population parameters, emphasizing the need to account for sampling errors and providing a measure of uncertainty in the estimated population mean.']}, {'end': 12656.543, 'start': 12074.369, 'title': 'Types of sampling techniques', 'summary': 'Discusses various sampling techniques including stratified sampling, proportionate sampling, and systematic sampling, highlighting the importance of maintaining a representative sample for accurate data analysis.', 'duration': 582.174, 'highlights': ['Stratified sampling involves dividing the population into subgroups and taking a random sample from each subgroup, ensuring a representative sample for accurate analysis. This method involves dividing the population into subgroups and taking a random sample from each, ensuring a representative sample for accurate analysis.', 'Proportionate sampling maintains the same proportions as the complete population, increasing the likelihood of the studies reflecting accurately on the entire population. Proportionate sampling maintains the same proportions as the complete population, increasing the likelihood of the studies reflecting accurately on the entire population.', 'Systematic sampling involves applying intervals between samples, useful for monitoring trends or identifying issues over time in processes or populations. Systematic sampling involves applying intervals between samples, useful for monitoring trends or identifying issues over time in processes or populations.']}, {'end': 13247.577, 'start': 12658.444, 'title': 'Clustering and student t distribution', 'summary': 'Covers the concept of clustering by calculating euclidean distances between data points to form clusters, and explains the application of student t distribution for samples with a size less than 30 and unknown standard deviation.', 'duration': 589.133, 'highlights': ['The chapter explains the process of clustering by calculating the Euclidean distance between data points to form clusters, demonstrating the concept with an example involving age, salary, and height data points. The process of clustering is demonstrated by calculating the Euclidean distance between data points, such as age, salary, and height, to form clusters.', 'The chapter discusses the application of student t distribution for samples with a size less than 30 and unknown standard deviation, providing an example of selecting cricketers for a team based on their scores. The application of student t distribution is explained for samples with a size less than 30 and unknown standard deviation, illustrated with an example of selecting cricketers for a team based on their scores.']}], 'duration': 2153.214, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo11094363.jpg', 'highlights': ['The importance of stratified sampling in building artificial neural network models on large datasets', 'The role of confidence intervals in estimating population parameters', 'The chapter discusses the process of selecting students for an institute, considering a scenario of 5,000 total students and 800 available seats', 'The chapter explains the process of clustering by calculating the Euclidean distance between data points to form clusters', 'The application of student t distribution is explained for samples with a size less than 30 and unknown standard deviation']}, {'end': 15040.856, 'segs': [{'end': 13293.192, 'src': 'embed', 'start': 13248.537, 'weight': 7, 'content': [{'end': 13253.86, 'text': 'Whatever the value that you get, you get some value, for sure T value from this.', 'start': 13248.537, 'duration': 5.323}, {'end': 13255.081, 'text': 'but how do you interpret that value?', 'start': 13253.86, 'duration': 1.221}, {'end': 13262.826, 'text': 'You are going to interpret that value in such a way that When you take this sample, let us say 20 cricketers you have taken,', 'start': 13255.321, 'duration': 7.505}, {'end': 13266.969, 'text': "so cricket board will come and ask you why didn't you take 20?", 'start': 13262.826, 'duration': 4.143}, {'end': 13268.49, 'text': 'You should have taken 60..', 'start': 13266.969, 'duration': 1.521}, {'end': 13277.616, 'text': 'Then you would say, sir, I cannot take 60 because the chances of spending the money on their trips will be going to be very high.', 'start': 13268.49, 'duration': 9.126}, {'end': 13281.538, 'text': 'Then why are you taking exactly 20 only?', 'start': 13278.517, 'duration': 3.021}, {'end': 13286.462, 'text': 'So how do you think that this 20 is actually representing the 100 cricketers?', 'start': 13282.279, 'duration': 4.183}, {'end': 13288.37, 'text': 'This is the question that comes to them.', 'start': 13287.23, 'duration': 1.14}, {'end': 13293.192, 'text': 'Then you will say, sir, I can take this 20.', 'start': 13288.991, 'duration': 4.201}], 'summary': 'Interpreting sample value for representing 100 cricketers is crucial for decision-making.', 'duration': 44.655, 'max_score': 13248.537, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo13248537.jpg'}, {'end': 14616.183, 'src': 'embed', 'start': 14575.532, 'weight': 0, 'content': [{'end': 14578.194, 'text': 'what is the formal definition of an algorithm?', 'start': 14575.532, 'duration': 2.662}, {'end': 14582.456, 'text': 'Well, guys, algorithms are as simple as this.', 'start': 14579.014, 'duration': 3.442}, {'end': 14584.718, 'text': 'They are just a set of rules,', 'start': 14582.877, 'duration': 1.841}, {'end': 14593.344, 'text': 'or you can call them as processes as well to be followed in calculations or any other problem solving operations when done by a computer.', 'start': 14584.718, 'duration': 8.626}, {'end': 14595.085, 'text': 'Well, how simple is that?', 'start': 14593.644, 'duration': 1.441}, {'end': 14597.847, 'text': 'Well, this is exactly what an algorithm means.', 'start': 14595.125, 'duration': 2.722}, {'end': 14600.369, 'text': 'well, you have a symbol on the left hand side.', 'start': 14597.847, 'duration': 2.522}, {'end': 14603.092, 'text': 'that is pretty much what a flow chart looks like as well.', 'start': 14600.369, 'duration': 2.723}, {'end': 14608.036, 'text': "uh, don't worry, you'll just be checking out the uh flow chart sections in the uh next set of this slide.", 'start': 14603.092, 'duration': 4.944}, {'end': 14612.36, 'text': 'but then, right now, i want to tell you guys that you guys are using algorithm, as is.', 'start': 14608.036, 'duration': 4.324}, {'end': 14614.201, 'text': "well, step one, you're looking at your screen.", 'start': 14612.36, 'duration': 1.841}, {'end': 14616.183, 'text': "well, you've programmed yourself to look at the screen.", 'start': 14614.201, 'duration': 1.982}], 'summary': 'An algorithm is a set of rules or processes for problem-solving in computer operations.', 'duration': 40.651, 'max_score': 14575.532, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo14575532.jpg'}, {'end': 14835.496, 'src': 'embed', 'start': 14806.964, 'weight': 3, 'content': [{'end': 14811.126, 'text': "here's the keyboard training themselves and again making predictions on that, right.", 'start': 14806.964, 'duration': 4.162}, {'end': 14811.626, 'text': 'so again,', 'start': 14811.126, 'duration': 0.5}, {'end': 14819.669, 'text': 'training and machine learning entails feeding a lot of data into the algorithm and allowing the machine itself to learn more about the process information.', 'start': 14811.626, 'duration': 8.043}, {'end': 14822.651, 'text': "well, you're going to just tell the machine a lot of basics, probably,", 'start': 14819.669, 'duration': 2.982}, {'end': 14829.874, 'text': 'or just show it one iteration where the machine pretty much goes on to figure out, say, nine or ten more iterations on its own.', 'start': 14822.651, 'duration': 7.223}, {'end': 14831.074, 'text': "it's going to learn on its own.", 'start': 14829.874, 'duration': 1.2}, {'end': 14835.496, 'text': "it's going to process on its own and pretty much you know you can work with that data later on, right?", 'start': 14831.074, 'duration': 4.422}], 'summary': 'Machine learning involves training with data and allowing the machine to learn and process information, potentially leading to 9-10 more iterations on its own.', 'duration': 28.532, 'max_score': 14806.964, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo14806964.jpg'}, {'end': 14958.055, 'src': 'embed', 'start': 14932.519, 'weight': 1, 'content': [{'end': 14942.407, 'text': "And then here we'll also be approximating the mapping function to a point where we'll have new input data coming in which we haven't seen which the machine hasn't seen.", 'start': 14932.519, 'duration': 9.888}, {'end': 14948.912, 'text': 'And then we can predict new output variables Y with respect to all the new data, the new X data.', 'start': 14942.867, 'duration': 6.045}, {'end': 14950.592, 'text': 'that the machine just saw.', 'start': 14949.352, 'duration': 1.24}, {'end': 14958.055, 'text': "So we've trained it for a particular amount of X's and then it saw a new amount of data, a new amount of input variables,", 'start': 14950.953, 'duration': 7.102}], 'summary': 'Approximating mapping function for new input data to predict new output variables.', 'duration': 25.536, 'max_score': 14932.519, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo14932519.jpg'}], 'start': 13248.537, 'title': 'Understanding statistical analysis and algorithms', 'summary': "Discusses interpreting t-values in statistical analysis for cricket player selection and iq distributions, probability distributions, p-value calculation, and algorithms' widespread use in recommendation systems and spam filtering, along with the introduction of machine learning and its main types.", 'chapters': [{'end': 13660.582, 'start': 13248.537, 'title': 'Interpreting t-values in statistical analysis', 'summary': 'Discusses the importance of interpreting t-values to represent a sample in statistical analysis, using the example of cricket player selection and comparing iq distributions of different cities, emphasizing the calculation of t-values and confidence intervals.', 'duration': 412.045, 'highlights': ['The significance of interpreting t-values to represent a sample in statistical analysis, such as in the example of selecting 20 cricketers to represent 100, emphasizing the importance of random sampling error being very less. 20 cricketers selected to represent 100, emphasizing very less random sampling error.', 'The calculation of t-values and confidence intervals to convince stakeholders about the representativeness of the sample, with t-values such as 0.3, 0.2, 1.2, or 1.3 being used and their corresponding probabilities defined in statistical tables. Calculation of t-values like 0.3, 0.2, 1.2, or 1.3 and their corresponding probabilities defined in statistical tables.', "The utilization of t-values to compare IQ distributions of different cities, such as Delhi and Bangalore, to ensure the best sample representation, with the calculation of t-values and p-values to determine the sample's quality and representation of the complete population. Comparison of IQ distributions of different cities using t-values and p-values to determine sample's quality and representation of the complete population."]}, {'end': 14093.578, 'start': 13661.522, 'title': 'Probability distributions and sampling', 'summary': 'Explains the concept of p-value calculation, the significance of p-value in determining sample accuracy, and the importance of understanding different probability distributions for real-world data science applications.', 'duration': 432.056, 'highlights': ['The chapter explains the concept of p-value calculation and its significance in determining sample accuracy, providing an example of a t-score calculator to demonstrate the process. The p-value calculation and its significance in determining sample accuracy is explained, with an example of a t-score calculator to demonstrate the process.', 'The importance of understanding different probability distributions for real-world data science applications is emphasized, with a focus on the ability to select the right distribution based on the specific problem at hand. The importance of understanding different probability distributions for real-world data science applications is emphasized, focusing on the ability to select the right distribution based on the specific problem at hand.', 'The example of determining the probability of fraudulent transactions in SBA ATMs is used to illustrate the necessity of choosing the appropriate distribution for specific problems, such as binomial or Poisson distribution. The example of determining the probability of fraudulent transactions in SBA ATMs is used to illustrate the necessity of choosing the appropriate distribution for specific problems, such as binomial or Poisson distribution.']}, {'end': 14707.664, 'start': 14094.278, 'title': 'Probability distributions and algorithms', 'summary': 'Discusses probability distributions, including poisson distribution, and algorithms, emphasizing the importance and widespread use of algorithms in various applications, such as recommendation systems and spam filtering, while also delving into the formal definition of an algorithm and its relationship with pseudocode and flowcharts.', 'duration': 613.386, 'highlights': ['The average number of days any patient stayed in the hospital is 4 days, with a probability of 15% for a patient to stay exactly 5 days and a cumulative probability of 62% for the patient to stay less than 5 days. The average length of hospital stay is 4 days, with a 15% probability for a patient to stay exactly 5 days and a cumulative probability of 62% for the patient to stay less than 5 days.', 'The Poisson distribution is used to calculate the probability of events such as car accidents and customer visits, with specific examples given, including a 13.5% probability of no car accidents when the average number of accidents is 2. The Poisson distribution is utilized to compute probabilities for events like car accidents and customer visits, with a 13.5% probability of no car accidents when the average number of accidents is 2.', 'The probability of the number of accidents occurring between 2 and 5 is 27%, with detailed calculations for individual probabilities provided, showcasing the versatility of the Poisson distribution in modeling various scenarios. The probability of the number of accidents occurring between 2 and 5 is 27%, demonstrating the flexibility of the Poisson distribution in modeling different situations.', 'The chapter emphasizes the fundamental role of algorithms in diverse contexts, from driving a car to email filtering, and highlights their historical significance and continued relevance in the field of computer science. The chapter underscores the essential role of algorithms in various contexts, such as driving and email filtering, and emphasizes their historical significance and ongoing relevance in computer science.', 'The discussion touches upon the formal definition of an algorithm as a set of rules or processes for problem-solving operations, illustrating its simplicity and universality in computational tasks. The chapter briefly covers the formal definition of an algorithm as a set of rules or processes for problem-solving operations, showcasing its simplicity and universality in computational tasks.']}, {'end': 15040.856, 'start': 14707.664, 'title': 'Pseudocode flowchart and machine learning', 'summary': 'Covers pseudocode flowchart relationship and introduces the concept of machine learning, highlighting the three main types of learning: supervised, unsupervised, and reinforcement learning, along with the goal and mapping function of supervised learning.', 'duration': 333.192, 'highlights': ['The chapter covers pseudocode flowchart relationship and introduces the concept of machine learning. The transcript discusses the pseudocode flowchart relationship and the introduction of machine learning, emphasizing its importance and application in the field.', 'Highlighting the three main types of learning: supervised, unsupervised, and reinforcement learning. The three main types of machine learning - supervised, unsupervised, and reinforcement learning - are introduced, emphasizing the need for understanding these concepts in the field of machine learning.', 'The goal and mapping function of supervised learning. The goal of the supervised learning system is explained, focusing on understanding the change in output variables with respect to input variables and approximating the mapping function for predicting new output variables.']}], 'duration': 1792.319, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo13248537.jpg', 'highlights': ["Comparison of IQ distributions of different cities using t-values and p-values to determine sample's quality and representation of the complete population.", 'The Poisson distribution is utilized to compute probabilities for events like car accidents and customer visits, with a 13.5% probability of no car accidents when the average number of accidents is 2.', 'The chapter underscores the essential role of algorithms in various contexts, such as driving and email filtering, and emphasizes their historical significance and ongoing relevance in computer science.', 'The average length of hospital stay is 4 days, with a 15% probability for a patient to stay exactly 5 days and a cumulative probability of 62% for the patient to stay less than 5 days.', "The utilization of t-values to compare IQ distributions of different cities, such as Delhi and Bangalore, to ensure the best sample representation, with the calculation of t-values and p-values to determine the sample's quality and representation of the complete population.", 'The chapter explains the concept of p-value calculation and its significance in determining sample accuracy, providing an example of a t-score calculator to demonstrate the process.', 'The importance of understanding different probability distributions for real-world data science applications is emphasized, focusing on the ability to select the right distribution based on the specific problem at hand.', 'The discussion touches upon the formal definition of an algorithm as a set of rules or processes for problem-solving operations, illustrating its simplicity and universality in computational tasks.', 'The goal of the supervised learning system is explained, focusing on understanding the change in output variables with respect to input variables and approximating the mapping function for predicting new output variables.', 'The three main types of machine learning - supervised, unsupervised, and reinforcement learning - are introduced, emphasizing the need for understanding these concepts in the field of machine learning.']}, {'end': 16166.596, 'segs': [{'end': 15609.533, 'src': 'embed', 'start': 15580.446, 'weight': 1, 'content': [{'end': 15585.707, 'text': "If you pretty much give it a biscuit at that moment, it will not realize if it's doing the right thing or the wrong thing right?", 'start': 15580.446, 'duration': 5.261}, {'end': 15589.167, 'text': "So that we can have a state of, let's say, the dog did not give a handshake.", 'start': 15585.987, 'duration': 3.18}, {'end': 15591.648, 'text': "And that's pretty much what ST means, guys.", 'start': 15589.607, 'duration': 2.041}, {'end': 15592.768, 'text': 'Reward is RT.', 'start': 15591.788, 'duration': 0.98}, {'end': 15599.329, 'text': "And this keeps on going in an iteration where you're just training your model better and better and better to hunt more rewards.", 'start': 15593.288, 'duration': 6.041}, {'end': 15602.83, 'text': "The more the rewards, then the machine's doing the right thing.", 'start': 15599.749, 'duration': 3.081}, {'end': 15604.51, 'text': "It's as simple as that, guys.", 'start': 15603.05, 'duration': 1.46}, {'end': 15609.533, 'text': 'So, on that note, I have two very simple demos, which are in Python,', 'start': 15605.21, 'duration': 4.323}], 'summary': 'Training model to hunt more rewards for better performance.', 'duration': 29.087, 'max_score': 15580.446, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo15580446.jpg'}, {'end': 16058.33, 'src': 'embed', 'start': 16030.062, 'weight': 0, 'content': [{'end': 16034.464, 'text': "right, this is for every single aspect that we're using to compare.", 'start': 16030.062, 'duration': 4.402}, {'end': 16038.946, 'text': "so let us quickly use describe to pretty much tell us what we're we're just looking at.", 'start': 16034.464, 'duration': 4.482}, {'end': 16043.467, 'text': 'and uh, yeah, so we have a count of about 3751 males.', 'start': 16038.946, 'duration': 4.521}, {'end': 16045.707, 'text': "yeah, it's going to give you the age of so many people.", 'start': 16043.467, 'duration': 2.24}, {'end': 16050.008, 'text': "it's going to give you all the cigarettes, bbmets, prevalence, stroke and so much more, right.", 'start': 16045.707, 'duration': 4.301}, {'end': 16055.509, 'text': 'so, coming to the process of logistic regression out here, from all this data set we need to make,', 'start': 16050.008, 'duration': 5.501}, {'end': 16058.33, 'text': 'we need to have an inference at the end of it right.', 'start': 16055.509, 'duration': 2.821}], 'summary': 'Using logistic regression on a dataset of 3751 males to infer various health factors.', 'duration': 28.268, 'max_score': 16030.062, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo16030062.jpg'}, {'end': 16097.5, 'src': 'embed', 'start': 16069.732, 'weight': 2, 'content': [{'end': 16073.973, 'text': 'as it already says, the 10-year CHDs pretty much are dependent variable.', 'start': 16069.732, 'duration': 4.241}, {'end': 16077.154, 'text': "We'll be using logistic regression so much more, right?", 'start': 16074.013, 'duration': 3.141}, {'end': 16081.355, 'text': "So it's going to give you all the standard errors, all the values of we call it the Z method.", 'start': 16077.414, 'duration': 3.941}, {'end': 16083.036, 'text': "It's going to give you the Z method value.", 'start': 16081.715, 'duration': 1.321}, {'end': 16092.138, 'text': "It's going to check if your probability of your outcome is greater than the value of Z with respect to all of these single categorical variables that we're checking.", 'start': 16083.096, 'duration': 9.042}, {'end': 16097.5, 'text': "And then when it comes to backward elimination, we'll pretty much be using our feature selection to go about doing it.", 'start': 16092.578, 'duration': 4.922}], 'summary': 'Using logistic regression to check chd probability with backward elimination for feature selection.', 'duration': 27.768, 'max_score': 16069.732, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo16069732.jpg'}, {'end': 16176.064, 'src': 'embed', 'start': 16148.446, 'weight': 3, 'content': [{'end': 16154.388, 'text': "right?. So 88.14% is a big number and it's been training well, not for many times, right?", 'start': 16148.446, 'duration': 5.942}, {'end': 16156.629, 'text': 'So the number of iterations again is very less.', 'start': 16154.428, 'duration': 2.201}, {'end': 16164.475, 'text': "so here's our subplot, is what we call as an axis subplot, and here as well you can pretty much check out the actual predicted outcome values.", 'start': 16157.009, 'duration': 7.466}, {'end': 16165.295, 'text': 'which is predicted?', 'start': 16164.475, 'duration': 0.82}, {'end': 16166.596, 'text': 'one predictor zero.', 'start': 16165.295, 'duration': 1.301}, {'end': 16171.34, 'text': 'the actual outcome values is this color, while the actual value is blue color right.', 'start': 16166.596, 'duration': 4.744}, {'end': 16176.064, 'text': "so the color distribution here again will let you know of what's going on there as well.", 'start': 16171.34, 'duration': 4.724}], 'summary': '88.14% accuracy achieved with few iterations, visualizing predicted outcome values.', 'duration': 27.618, 'max_score': 16148.446, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo16148446.jpg'}], 'start': 15040.856, 'title': 'Machine learning concepts and demonstrations', 'summary': 'Covers supervised and unsupervised learning, k-means clustering, reinforcement learning, logistic regression, and includes a demo achieving 88.14% accuracy in predicting heart disease.', 'chapters': [{'end': 15333.9, 'start': 15040.856, 'title': 'Supervised and unsupervised learning', 'summary': 'Discusses the concepts of supervised learning, including classification and regression, with examples of logistic regression and gender classification, as well as providing an overview of unsupervised learning.', 'duration': 293.044, 'highlights': ['In supervised learning, regression is a type where the output variable is a continuous numeric value, exemplified by predicting the cost of apples based on various factors.', 'Logistic regression in supervised learning involves a dependent variable that is a categorical value, with a binary outcome, depicted by a model with an S-shaped curve graph.', 'Classification in supervised learning involves categorically analyzing data to determine specific outcomes, as demonstrated by identifying the gender of a person based on various factors.', 'Unsupervised learning involves algorithms with input data that has no labels, making it challenging for the machine to understand the data easily.']}, {'end': 15756.93, 'start': 15334.64, 'title': 'Unsupervised learning and reinforcement learning', 'summary': 'Discusses unsupervised learning, particularly focusing on the k-means clustering algorithm, and then delves into reinforcement learning, explaining the concept and providing real-life examples of its application.', 'duration': 422.29, 'highlights': ['K-means Clustering Algorithm The unsupervised learning algorithm, k-means clustering, aims to group similar data points into clusters, with a high intra-cluster similarity and low inter-cluster similarity. The optimal number of clusters is determined using the elbow method, with a demonstration using Python.', 'Reinforcement Learning Reinforcement learning involves an agent performing actions in an environment, receiving rewards for correct actions, and adjusting its behavior based on the received rewards. Real-life examples, including Pac-Man and training animals, illustrate the concept.', 'Google Collab and Python Demos The speaker presents practical demonstrations using Python in Google Collab, showcasing the application of machine learning algorithms, specifically k-means clustering, to generate data, determine optimal clusters, and train the model.']}, {'end': 16166.596, 'start': 15757.33, 'title': 'Machine learning: logistic regression demo', 'summary': 'Showcases a demonstration of using k-means clustering and logistic regression to predict heart disease, including exploratory analysis, data cleaning, model training, and achieving a model accuracy of 88.14%.', 'duration': 409.266, 'highlights': ['The chapter showcases a demonstration of using k-means clustering and logistic regression to predict heart disease. The demonstration covers the use of k-means clustering to find four clusters and the application of logistic regression to predict heart disease using the Framingham dataset.', "The model achieves a high accuracy of 88.14% after training. After splitting the dataset into training and testing sets, the model's accuracy is found to be 88.14% using the Skykit learn library.", 'Exploratory analysis and data cleaning are performed on the dataset. Exploratory analysis is conducted to understand the distribution of data, and data cleaning involves handling missing values, with 388 missing values for glucose and 50 missing values for cholesterol.']}], 'duration': 1125.74, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo15040856.jpg', 'highlights': ['Demo achieves 88.14% accuracy in predicting heart disease using k-means clustering and logistic regression', 'K-means clustering groups similar data points into clusters with high intra-cluster similarity and low inter-cluster similarity', 'Reinforcement learning involves an agent performing actions in an environment, receiving rewards, and adjusting its behavior', 'Logistic regression in supervised learning involves a dependent variable that is a categorical value with a binary outcome', 'Supervised learning regression predicts the cost of apples based on various factors']}, {'end': 18627.153, 'segs': [{'end': 16199.06, 'src': 'embed', 'start': 16166.596, 'weight': 4, 'content': [{'end': 16171.34, 'text': 'the actual outcome values is this color, while the actual value is blue color right.', 'start': 16166.596, 'duration': 4.744}, {'end': 16176.064, 'text': "so the color distribution here again will let you know of what's going on there as well.", 'start': 16171.34, 'duration': 4.724}, {'end': 16182.168, 'text': "well, here is another step to pretty much print out what's uh, you know what's a true, uh, true, positive rate of the data,", 'start': 16176.064, 'duration': 6.104}, {'end': 16188.973, 'text': 'true negative rate of the data and so much more, to put it all into one single print statement to make it sure it looks very nicely.', 'start': 16182.168, 'duration': 6.805}, {'end': 16191.635, 'text': 'The accuracy of our entire model is about 88%.', 'start': 16189.353, 'duration': 2.282}, {'end': 16199.06, 'text': "The misclassification is pretty much one minus what the accuracy is, right? So we've missed about 11% of accuracy.", 'start': 16191.635, 'duration': 7.425}], 'summary': 'Analyzing model performance with 88% accuracy and 11% misclassification rate.', 'duration': 32.464, 'max_score': 16166.596, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo16166596.jpg'}, {'end': 16372.895, 'src': 'embed', 'start': 16329.966, 'weight': 1, 'content': [{'end': 16334.628, 'text': "okay, due to some extra features that we don't get in normal lists in python.", 'start': 16329.966, 'duration': 4.662}, {'end': 16338.989, 'text': 'okay, so numpy can support 1d, 2d, both of the kinds of arrays.', 'start': 16334.628, 'duration': 4.361}, {'end': 16343.634, 'text': 'okay, and i think you might be using this anaconda version of python.', 'start': 16339.569, 'duration': 4.065}, {'end': 16348.98, 'text': 'so that will be coming along with pandas, and i mean numpy version pre-installed.', 'start': 16343.634, 'duration': 5.346}, {'end': 16352.344, 'text': 'okay, numpy will be pre-installed in the python versions.', 'start': 16348.98, 'duration': 3.364}, {'end': 16353.946, 'text': 'okay, i mean anaconda python.', 'start': 16352.344, 'duration': 1.602}, {'end': 16357.51, 'text': 'okay, now, when you use a package, right, what we do?', 'start': 16353.946, 'duration': 3.564}, {'end': 16359.032, 'text': 'we import the package.', 'start': 16357.51, 'duration': 1.522}, {'end': 16363.107, 'text': 'okay, we import the package with something like we.', 'start': 16359.032, 'duration': 4.075}, {'end': 16364.828, 'text': 'the syntax is, it can be any package.', 'start': 16363.107, 'duration': 1.721}, {'end': 16366.49, 'text': 'okay, alias can be anything.', 'start': 16364.828, 'duration': 1.662}, {'end': 16367.491, 'text': 'so what is the syntax?', 'start': 16366.49, 'duration': 1.001}, {'end': 16370.253, 'text': 'syntax is import, then there will be package name.', 'start': 16367.491, 'duration': 2.762}, {'end': 16372.895, 'text': 'then there will be ask keyword, just to assign alias.', 'start': 16370.253, 'duration': 2.642}], 'summary': 'Numpy in python supports 1d and 2d arrays, pre-installed in anaconda with pandas.', 'duration': 42.929, 'max_score': 16329.966, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo16329966.jpg'}, {'end': 16696.865, 'src': 'embed', 'start': 16668.317, 'weight': 8, 'content': [{'end': 16674.878, 'text': 'okay, this part we will see a comparison in the later, later half of the section and then we can understand, like where it is beneficial,', 'start': 16668.317, 'duration': 6.561}, {'end': 16677.719, 'text': 'rather than storing it in a classical list.', 'start': 16674.878, 'duration': 2.841}, {'end': 16685.141, 'text': 'uh, then, numpy array, okay now, next is, if you go into check the individual, yes, right, so it will be.', 'start': 16677.719, 'duration': 7.422}, {'end': 16686.561, 'text': 'begin colon n minus one.', 'start': 16685.141, 'duration': 1.42}, {'end': 16687.042, 'text': 'that is how.', 'start': 16686.561, 'duration': 0.481}, {'end': 16690.123, 'text': 'so right hand side will always be excluded like that.', 'start': 16687.042, 'duration': 3.081}, {'end': 16696.865, 'text': 'okay, next, is numpy array initialization right, so how do we initialize numpy array, as we have already seen.', 'start': 16690.123, 'duration': 6.742}], 'summary': 'Comparison of storage options in numpy array vs classical list.', 'duration': 28.548, 'max_score': 16668.317, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo16668317.jpg'}, {'end': 16934.119, 'src': 'embed', 'start': 16903.403, 'weight': 14, 'content': [{'end': 16904.384, 'text': 'we want six numbers.', 'start': 16903.403, 'duration': 0.981}, {'end': 16907.726, 'text': 'it is almost same as numpy, but it has a bit difference.', 'start': 16904.384, 'duration': 3.342}, {'end': 16910.027, 'text': 'so how it is differing i will show you.', 'start': 16907.726, 'duration': 2.301}, {'end': 16919.09, 'text': 'okay. so this one now, in the arrange, the last, the end number can we put as 19 instead of 20 in the last example?', 'start': 16910.027, 'duration': 9.063}, {'end': 16922.393, 'text': 'yeah, then it will print only single number, right.', 'start': 16919.09, 'duration': 3.303}, {'end': 16926.376, 'text': 'the second argument yeah, yeah, this one we can.', 'start': 16922.393, 'duration': 3.983}, {'end': 16928.097, 'text': 'it will go till 18 only, not an issue.', 'start': 16926.376, 'duration': 1.721}, {'end': 16928.976, 'text': 'Okay, thanks.', 'start': 16928.476, 'duration': 0.5}, {'end': 16930.397, 'text': 'Yeah, you can put anything.', 'start': 16929.377, 'duration': 1.02}, {'end': 16934.119, 'text': 'So the logic is when it reaches the end number, right?', 'start': 16930.417, 'duration': 3.702}], 'summary': 'Discussion about numpy and differences with examples, considering 19 instead of 20 in the last example.', 'duration': 30.716, 'max_score': 16903.403, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo16903403.jpg'}, {'end': 18098.668, 'src': 'embed', 'start': 18045.718, 'weight': 7, 'content': [{'end': 18053.304, 'text': 'It will compare those on a broader level, that is, on a array level, and it will give you only single output of false or true or false,', 'start': 18045.718, 'duration': 7.586}, {'end': 18054.686, 'text': 'based on the comparison results.', 'start': 18053.304, 'duration': 1.382}, {'end': 18056.187, 'text': "Okay So that's how it is.", 'start': 18054.946, 'duration': 1.241}, {'end': 18057.388, 'text': "That's how it works.", 'start': 18056.687, 'duration': 0.701}, {'end': 18062.793, 'text': 'Okay Next is aggregate function on this on a single array.', 'start': 18057.989, 'duration': 4.804}, {'end': 18065.135, 'text': 'Aggregate functions always work on a single array.', 'start': 18062.933, 'duration': 2.202}, {'end': 18067.317, 'text': "So that's what we are going to see next.", 'start': 18065.635, 'duration': 1.682}, {'end': 18078.341, 'text': 'Now, if we have already seen this sum function, right? So..', 'start': 18070.458, 'duration': 7.883}, {'end': 18082.322, 'text': 'Different dimensions when you use equal or other things.', 'start': 18078.341, 'duration': 3.981}, {'end': 18084.423, 'text': 'Sorry, you mean this right?', 'start': 18082.962, 'duration': 1.461}, {'end': 18092.846, 'text': "Yeah, so no, I mean, when you're doing an equal comparison, yeah, so how did it handle different dimensions of the arrays?", 'start': 18084.723, 'duration': 8.123}, {'end': 18094.146, 'text': "It won't handle right?", 'start': 18093.266, 'duration': 0.88}, {'end': 18098.668, 'text': 'It will throw you an error, because that is how matrix works in real world, right?', 'start': 18094.406, 'duration': 4.262}], 'summary': 'Comparison and aggregation functions work on arrays, yielding single true/false output. equal comparisons handle different array dimensions, resulting in errors.', 'duration': 52.95, 'max_score': 18045.718, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo18045718.jpg'}, {'end': 18529.736, 'src': 'embed', 'start': 18498.263, 'weight': 0, 'content': [{'end': 18501.885, 'text': 'so index in python list refers to a integer position.', 'start': 18498.263, 'duration': 3.622}, {'end': 18505.707, 'text': 'right, it can either start from minus or it can start from plus.', 'start': 18501.885, 'duration': 3.822}, {'end': 18507.708, 'text': 'so minus when?', 'start': 18505.707, 'duration': 2.001}, {'end': 18513.29, 'text': 'when we go for so python, when, uh, positive indexing is done,', 'start': 18507.708, 'duration': 5.582}, {'end': 18520.993, 'text': 'then the index goes from smaller number to higher number and then prints accordingly right.', 'start': 18513.29, 'duration': 7.703}, {'end': 18529.736, 'text': "so let's say for this monty python we are going from 6 to 10, so that will take from p till h.", 'start': 18520.993, 'duration': 8.743}], 'summary': 'Python list indexing can start from positive or negative numbers, with positive indexing going from smaller to higher numbers.', 'duration': 31.473, 'max_score': 18498.263, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo18498263.jpg'}], 'start': 16166.596, 'title': 'Data analysis, machine learning, and numpy usage in python', 'summary': 'Discusses the accuracy, misclassification, true positive and negative rates of a machine learning model with an overall accuracy of 88%. it highlights the usage of k-means, clustering, and logistic regression algorithms, and emphasizes the importance of numpy and scipy packages in data analysis and machine learning. additionally, it explains how to use numpy to install and manipulate 2d arrays in python, covers the initialization of numpy arrays, the usage of numpy functions, and numpy array operations including array broadcasting, indexing, and slicing in python.', 'chapters': [{'end': 16454.524, 'start': 16166.596, 'title': 'Data analysis and machine learning with python', 'summary': 'Discusses the accuracy, misclassification, true positive and negative rates of a machine learning model, with an overall accuracy of 88%. it also highlights the usage of k-means, clustering and logistic regression algorithms, as well as the importance of numpy and scipy packages in data analysis and machine learning.', 'duration': 287.928, 'highlights': ['The accuracy of our entire model is about 88%. The accuracy of the machine learning model is approximately 88%, indicating its overall performance.', 'True negative rates we have somewhere around 99%. The true negative rates of the model are approximately 99%, demonstrating its ability to accurately predict negative outcomes.', 'Positive prediction rate is 80%. The positive prediction rate of the model is approximately 80%, indicating its ability to accurately predict positive outcomes.', 'The misclassification is pretty much one minus what the accuracy is, right? The misclassification rate of the model is approximately 12%, calculated as the difference between 100% and the accuracy of 88%.', 'The chapter discusses the accuracy, misclassification, true positive and negative rates of a machine learning model, with an overall accuracy of 88%. The chapter provides insights into the accuracy, misclassification, true positive, and true negative rates of a machine learning model, with an overall accuracy of 88%.', 'The chapter highlights the usage of k-means, clustering and logistic regression algorithms, as well as the importance of numpy and scipy packages in data analysis and machine learning. The chapter emphasizes the usage of k-means, clustering, and logistic regression algorithms, along with the significance of numpy and scipy packages in the field of data analysis and machine learning.']}, {'end': 16690.123, 'start': 16454.524, 'title': 'Using numpy for 2d arrays in python', 'summary': 'Explains how to use numpy to install and manipulate 2d arrays in python, emphasizing the advantages of using numpy arrays and how they are beneficial in terms of memory usage.', 'duration': 235.599, 'highlights': ["NumPy installation using pip install numpy Explains the process of installing NumPy using the command 'pip install numpy', highlighting the essential step for using NumPy for 2D arrays in Python.", 'Creation of 2D arrays in NumPy Describes the method of creating 2D arrays in NumPy by passing two lists and concatenating them, illustrating the process of changing lists into 2D arrays.', 'Advantages of using NumPy arrays Emphasizes the advantages of using NumPy arrays, including the conversion of data type from mutable to non-mutable, and the efficient memory usage with the same size of block for all stored objects.']}, {'end': 17820.237, 'start': 16690.123, 'title': 'Numpy array initialization', 'summary': 'Covers the initialization of numpy arrays, including creating zero-based arrays, using np.zeros, creating arrays with intervals using np.array, and spreading points over a straight line using np.arange, with a focus on the dimension and data type of the arrays.', 'duration': 1130.114, 'highlights': ['Creating zero-based arrays Explains the process of creating zero-based arrays using np.zeros and specifying the dimensions, such as 3 rows and 4 columns.', 'Creating arrays with intervals Describes the creation of arrays with intervals using np.array, specifying the start, end, and interval, with examples of the resulting array.', 'Spreading points over a straight line Demonstrates the use of np.arange to spread points between a specified range, including the exclusion of the endpoint and options for different intervals.', 'Accessing and modifying array shape Discusses the shape function for accessing the dimensions of the array, modifying the shape, and accessing individual elements of the shape tuple.', 'Understanding data type of arrays Explains the d type function to determine the data type of elements in the array, highlighting the homogeneous nature of numpy arrays.', 'Performing mathematical calculations with numpy arrays Covers the usage of numpy functions like np.sum to perform mathematical operations such as addition and subtraction on arrays, including options for summing based on rows or columns.']}, {'end': 18207.02, 'start': 17820.237, 'title': 'Numpy functions and usage', 'summary': 'Covers the usage of numpy functions, including element-wise operations, aggregate functions, and their relevance in data science, with a focus on matrix operations and statistical functions for data analysis.', 'duration': 386.783, 'highlights': ['Numpy functions include element-wise operations like multiplication and division, mirroring matrix operations in mathematics, with examples of 5 into 2 resulting in 10 and 10 by 3. It explains the element-wise multiplication and division similar to matrix operations in mathematics, with examples of 5 into 2 resulting in 10 and 10 by 3.', 'The chapter discusses aggregate functions such as mean, median, mode, and standard deviation, emphasizing their importance in data science and the need for a strong foundation in math and statistics. It emphasizes the importance of aggregate functions such as mean, median, mode, and standard deviation in data science, highlighting the need for a strong foundation in math and statistics.', 'The transcript also covers the concept of element-wise comparison and array-level comparison, providing insights into the practical usage of np.equal and its output of true or false for array comparisons. It provides insights into the practical usage of np.equal for element-wise and array-level comparisons, showcasing the output as true or false for array comparisons.']}, {'end': 18627.153, 'start': 18208.795, 'title': 'Numpy array operations and array broadcasting', 'summary': 'Covers numpy array operations including sum, minimum, maximum, mean, and standard deviation, and explains the concept of array broadcasting in numpy, along with indexing and slicing in python.', 'duration': 418.358, 'highlights': ['The chapter covers numpy array operations including sum, minimum, maximum, mean, and standard deviation. It explains the functions of sum, minimum, maximum, mean, and standard deviation in numpy.', 'It explains the concept of array broadcasting in numpy, which inflates the numpy array to match the dimension of the first array for feasible operations. The concept of array broadcasting in numpy is detailed, illustrating how the numpy array gets inflated to match the dimension of the first array for feasible operations.', 'The chapter also covers indexing and slicing in Python, detailing the concepts of index and slicing in Python lists. Indexing and slicing in Python lists are explained, with examples of positive and negative indexing, and explanations of slicing.']}], 'duration': 2460.557, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo16166596.jpg', 'highlights': ['The accuracy of our entire model is about 88%.', 'The true negative rates of the model are approximately 99%.', 'The positive prediction rate of the model is approximately 80%.', 'The misclassification rate of the model is approximately 12%.', 'The chapter emphasizes the usage of k-means, clustering, and logistic regression algorithms.', 'The chapter provides insights into the accuracy, misclassification, true positive, and true negative rates of a machine learning model, with an overall accuracy of 88%.', 'The chapter discusses the accuracy, misclassification, true positive and negative rates of a machine learning model, with an overall accuracy of 88%.', 'The chapter highlights the usage of k-means, clustering and logistic regression algorithms, as well as the importance of numpy and scipy packages in data analysis and machine learning.', 'Emphasizes the advantages of using NumPy arrays, including the conversion of data type from mutable to non-mutable, and the efficient memory usage with the same size of block for all stored objects.', "Explains the process of installing NumPy using the command 'pip install numpy', highlighting the essential step for using NumPy for 2D arrays in Python.", 'Describes the method of creating 2D arrays in NumPy by passing two lists and concatenating them, illustrating the process of changing lists into 2D arrays.', 'Explains the process of creating zero-based arrays using np.zeros and specifying the dimensions, such as 3 rows and 4 columns.', 'Covers the usage of numpy functions like np.sum to perform mathematical operations such as addition and subtraction on arrays, including options for summing based on rows or columns.', 'It emphasizes the importance of aggregate functions such as mean, median, mode, and standard deviation in data science, highlighting the need for a strong foundation in math and statistics.', 'Provides insights into the practical usage of np.equal for element-wise and array-level comparisons, showcasing the output as true or false for array comparisons.', 'The concept of array broadcasting in numpy is detailed, illustrating how the numpy array gets inflated to match the dimension of the first array for feasible operations.', 'Indexing and slicing in Python lists are explained, with examples of positive and negative indexing, and explanations of slicing.']}, {'end': 21215.258, 'segs': [{'end': 19687.928, 'src': 'embed', 'start': 19644.153, 'weight': 2, 'content': [{'end': 19648.555, 'text': "OK, for vertical stacking, it's the same thing, but on the column basis.", 'start': 19644.153, 'duration': 4.402}, {'end': 19650.196, 'text': 'OK, this is now a single column.', 'start': 19648.715, 'duration': 1.481}, {'end': 19651.857, 'text': 'This is now a single column like that.', 'start': 19650.236, 'duration': 1.621}, {'end': 19655.599, 'text': 'And for vertical concatenation, it is the same thing as horizontal stack.', 'start': 19651.997, 'duration': 3.602}, {'end': 19657.339, 'text': 'It added those elements in there.', 'start': 19655.799, 'duration': 1.54}, {'end': 19661.181, 'text': 'OK, so that is what is about this four operations on this array.', 'start': 19657.5, 'duration': 3.681}, {'end': 19665.123, 'text': 'OK, so is this clear now? OK, next is column stack.', 'start': 19661.342, 'duration': 3.781}, {'end': 19666.084, 'text': 'It is same thing.', 'start': 19665.384, 'duration': 0.7}, {'end': 19687.928, 'text': "let's see it here, only to go in the other slides.", 'start': 19666.462, 'duration': 21.466}], 'summary': 'Explanation of vertical and horizontal stacking operations on arrays.', 'duration': 43.775, 'max_score': 19644.153, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo19644153.jpg'}, {'end': 20736.867, 'src': 'embed', 'start': 20711.949, 'weight': 1, 'content': [{'end': 20718.854, 'text': "that's why python is again again, very popular, and today's pandas module this is this has many functions,", 'start': 20711.949, 'duration': 6.905}, {'end': 20721.996, 'text': 'many things related to data manipulation and data analysis.', 'start': 20718.854, 'duration': 3.142}, {'end': 20724.078, 'text': "so that's why we use it, and it's open source.", 'start': 20721.996, 'duration': 2.082}, {'end': 20725.619, 'text': "that's the main reason we use it.", 'start': 20724.078, 'duration': 1.541}, {'end': 20729.082, 'text': "okay, so let's go ahead and let's see how this pandas works.", 'start': 20725.619, 'duration': 3.463}, {'end': 20730.963, 'text': 'okay, how this panda was derived, actually.', 'start': 20729.082, 'duration': 1.881}, {'end': 20734.866, 'text': "so this panda's name was derived from the what panel data?", 'start': 20730.963, 'duration': 3.903}, {'end': 20736.867, 'text': 'Okay, what is what panel data that?', 'start': 20735.086, 'duration': 1.781}], 'summary': "Python's pandas module is popular for data manipulation and analysis, being open source. it's named after panel data.", 'duration': 24.918, 'max_score': 20711.949, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo20711949.jpg'}, {'end': 20897.172, 'src': 'embed', 'start': 20872.351, 'weight': 0, 'content': [{'end': 20877.897, 'text': "but we don't really use 3d data nowadays because that's kind of things we still don't get.", 'start': 20872.351, 'duration': 5.546}, {'end': 20879.638, 'text': 'that is basically for videos and all.', 'start': 20877.897, 'duration': 1.741}, {'end': 20882.882, 'text': 'where we have layers of data, right pixels are layered.', 'start': 20879.638, 'duration': 3.244}, {'end': 20885.944, 'text': "so those kind of data we don't really get it in data things.", 'start': 20882.882, 'duration': 3.062}, {'end': 20890.707, 'text': 'I mean video analysis is only used for traffic, police is for police purpose, not much,', 'start': 20885.944, 'duration': 4.763}, {'end': 20894.97, 'text': 'and others very high-end jobs that will be using this kind of data.', 'start': 20890.707, 'duration': 4.263}, {'end': 20897.172, 'text': "so for normal use it we don't have it.", 'start': 20894.97, 'duration': 2.202}], 'summary': '3d data not widely used for general purposes, mainly for videos and high-end jobs.', 'duration': 24.821, 'max_score': 20872.351, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo20872351.jpg'}], 'start': 18627.153, 'title': 'Numpy and pandas array operations', 'summary': 'Covers slicing in 2d arrays, numpy array indexing, manipulation methods, advantages of numpy arrays over lists, including a 41% faster speed, and the wide range of libraries in python for data science, as well as pandas functionality for efficient data management and analysis.', 'chapters': [{'end': 18725.798, 'start': 18627.153, 'title': 'Slicing in 2d arrays', 'summary': "Discusses the slicing of a 2d array, where 'colon one' returns the first row and 'colon one' extracts till the first column.", 'duration': 98.645, 'highlights': ["Slicing in 2D arrays involves using 'colon' notation for row and column extraction, where 'colon one' returns the first row and 'colon one' extracts till the first column.", 'The indexing in 2D arrays is comma-separated, with the first part working for rows and the second part for columns, as demonstrated by the slicing process.', "The specific example of 'colon one' returning the first row and 'colon one' extracting till the first column is explained, providing a clear understanding of the slicing behavior."]}, {'end': 19082.589, 'start': 18725.798, 'title': 'Numpy array indexing and array manipulation', 'summary': 'Explains numpy array indexing and slicing, including examples and detailed explanations, followed by a discussion on array manipulation methods such as concatenation, vstack, and hstack for numpy arrays.', 'duration': 356.791, 'highlights': ['The chapter explains numpy array indexing and slicing, including examples and detailed explanations. It covers the concept of indexing and slicing in numpy arrays, demonstrating the use of colons to select specific rows and columns.', 'Detailed explanations of array manipulation methods such as concatenation, VStack, and HStack for NumPy arrays. It provides detailed explanations of array manipulation methods, including concatenation, VStack, and HStack, with examples and visual representations.']}, {'end': 20462.683, 'start': 19082.589, 'title': 'Understanding numpy arrays and operations', 'summary': "Explains the concepts of stacking, concatenation, splitting of arrays, and the advantages of numpy over lists. it also provides examples of memory space and operation speed differences between lists and numpy arrays, highlighting numpy's significant performance advantages.", 'duration': 1380.094, 'highlights': ['Numpy arrays consume less memory compared to lists; for example, storing 1000 elements in a Python list takes up 28000 bytes, whereas for a numpy array, it takes up only 4000 bytes. Storing 1000 elements in a Python list takes up 28000 bytes, whereas for a numpy array, it takes up only 4000 bytes.', 'Numpy arrays are significantly faster than lists in performing operations; for example, for the same operation, the time difference for numpy arrays is 0.000999 compared to 0.005 for lists. For the same operation, the time difference for numpy arrays is 0.000999 compared to 0.005 for lists.', 'The chapter explains the concepts of stacking, concatenation, and splitting of arrays, providing a comprehensive understanding of how these operations work with numpy arrays. The chapter explains the concepts of stacking, concatenation, and splitting of arrays, providing a comprehensive understanding of how these operations work with numpy arrays.']}, {'end': 20812.017, 'start': 20462.683, 'title': 'Numpy arrays and python for data science', 'summary': 'Discusses the speed difference between numpy arrays and python lists, highlighting the 41% faster speed of numpy arrays, the direct storage of data in memory locations in numpy arrays, and the importance of strong processor cores for data scientists. it also emphasizes the wide range of libraries in python for data science, the availability of open-source packages, and the ease of implementing machine learning algorithms using these packages.', 'duration': 349.334, 'highlights': ['The chapter highlights the 41% faster speed of numpy arrays compared to Python lists, with a specific example of a one lakh data set.', 'The direct storage of data in memory locations in numpy arrays is explained, emphasizing the direct storage of values in RAM cells, making it one level storage.', 'The importance of strong processor cores for data scientists is emphasized, stating that the processor handles the request and processes data with the help of hardware and RAM.', 'The wide range of libraries in Python for data science is emphasized, mentioning that they help in accomplishing tasks in a short time without starting from scratch.', 'The availability of open-source packages in Python for data science is highlighted, emphasizing that they are free to use and eliminate the need to develop everything from scratch.', "The ease of implementing machine learning algorithms using Python's packages is emphasized, stating that it saves a significant amount of time and effort.", 'The creation and features of the pandas module are discussed, including its derivation from panel data and its use for time series data analysis, along with its creation in 2015 and its reliance on python lists, dictionaries, and numpy arrays.']}, {'end': 21215.258, 'start': 20812.017, 'title': 'Pandas data handling', 'summary': 'Explains the usage of pandas for handling multi-dimensional data, handling missing data, data alignment, group by functionality, input/output tools, time series functionality, and the differences between pandas and numpy, offering a wide array of functionalities for efficient data management and analysis.', 'duration': 403.241, 'highlights': ['Pandas supports multi-dimensional data, including 1D, 2D, and 3D data. Pandas can support multi-dimensional data, 2d data and one-dimensional data, including series object for 1d data, and data frame for tabular 2d data.', 'Pandas provides functions to handle missing data by filling in the missing values with the mean of the dataset. Pandas offers handy functions to replace missing or null data with the mean of the dataset, making handling of missing data very easy.', 'Pandas offers group by, pivot, join, and merge functionalities without the need for a relational database. Pandas module provides group by, pivot, join, and merge functionalities, enabling operations without requiring a relational database.', 'Pandas provides robust input/output tools to handle various file formats such as xls, xlsx, csv, and text files, allowing easy conversion of data frames and series objects to excel files. Pandas offers robust input/output tools to handle various file formats, enabling easy conversion of data frames and series objects to excel files with just one line of code.', 'Pandas provides time series specific functionality and is suitable for accommodating heterogeneous data, including strings, making it useful for text processing and analysis. Pandas offers time series specific functionality and is suitable for accommodating heterogeneous data, including strings, making it useful for text processing and analysis.']}], 'duration': 2588.105, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo18627153.jpg', 'highlights': ['Numpy arrays consume less memory compared to lists; for example, storing 1000 elements in a Python list takes up 28000 bytes, whereas for a numpy array, it takes up only 4000 bytes.', 'The chapter highlights the 41% faster speed of numpy arrays compared to Python lists, with a specific example of a one lakh data set.', 'Pandas provides robust input/output tools to handle various file formats such as xls, xlsx, csv, and text files, allowing easy conversion of data frames and series objects to excel files.', 'Pandas offers group by, pivot, join, and merge functionalities without the need for a relational database.', 'Pandas supports multi-dimensional data, including 1D, 2D, and 3D data.']}, {'end': 23836.651, 'segs': [{'end': 21582.633, 'src': 'embed', 'start': 21552.218, 'weight': 0, 'content': [{'end': 21554.48, 'text': 'okay, so yeah, you can check it like that.', 'start': 21552.218, 'duration': 2.262}, {'end': 21556.382, 'text': 'next is, the series object is done.', 'start': 21554.48, 'duration': 1.902}, {'end': 21560.185, 'text': 'so it will be of type pandas dot core dot, cds dot, cds.', 'start': 21556.382, 'duration': 3.803}, {'end': 21562.006, 'text': "okay, so that's how it will work.", 'start': 21560.185, 'duration': 1.821}, {'end': 21563.928, 'text': 'next is how to change index times.', 'start': 21562.006, 'duration': 1.922}, {'end': 21566.61, 'text': 'okay, this was what i was talking about.', 'start': 21563.928, 'duration': 2.682}, {'end': 21576.169, 'text': 'so that means, like you saw, we had the 0, 1, 2, 3 index kind of.', 'start': 21566.61, 'duration': 9.559}, {'end': 21576.809, 'text': 'i mean in.', 'start': 21576.169, 'duration': 0.64}, {'end': 21579.851, 'text': 'i mean integer kind of labels attached to each row.', 'start': 21576.809, 'duration': 3.042}, {'end': 21582.633, 'text': 'but now you can change it to anything.', 'start': 21579.851, 'duration': 2.782}], 'summary': 'Demonstrates changing index labels in pandas series.', 'duration': 30.415, 'max_score': 21552.218, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo21552218.jpg'}, {'end': 21945.272, 'src': 'embed', 'start': 21916.147, 'weight': 5, 'content': [{'end': 21917.509, 'text': 'so what it will do with the rest?', 'start': 21916.147, 'duration': 1.362}, {'end': 21918.972, 'text': 'it will put an end in there.', 'start': 21917.509, 'duration': 1.463}, {'end': 21926.248, 'text': "so that's how this panda's data i mean this 2d arrays has been mapped to a, like panda's object.", 'start': 21918.972, 'duration': 7.276}, {'end': 21930.389, 'text': 'okay, now let me change this to dictionary right away.', 'start': 21926.248, 'duration': 4.141}, {'end': 21935.01, 'text': 'so here, if you see, i have put in a dictionary right, a and b.', 'start': 21930.389, 'duration': 4.621}, {'end': 21944.212, 'text': 'so a, b are converted to columns and others have been changed to row indexes 0, 1, 2, 3, 4, and the values are mapped to this.', 'start': 21935.01, 'duration': 9.202}, {'end': 21945.272, 'text': 'columns only.', 'start': 21944.212, 'duration': 1.06}], 'summary': '2d array mapped to a pandas object, then converted to dictionary.', 'duration': 29.125, 'max_score': 21916.147, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo21916147.jpg'}, {'end': 22083.155, 'src': 'embed', 'start': 22054.359, 'weight': 8, 'content': [{'end': 22058.04, 'text': 'this is a real world thing where we use this dictionaries.', 'start': 22054.359, 'duration': 3.681}, {'end': 22062.601, 'text': 'okay, so for fruit we have some values and for count we have some values.', 'start': 22058.04, 'duration': 4.561}, {'end': 22066.423, 'text': 'so that means we have 10 apples, 12 mangoes and 13 bananas.', 'start': 22062.601, 'duration': 3.822}, {'end': 22068.265, 'text': 'in that case it will show up nicely.', 'start': 22066.423, 'duration': 1.842}, {'end': 22072.407, 'text': 'okay, the example that i have taken, it, is something else.', 'start': 22068.265, 'duration': 4.142}, {'end': 22074.209, 'text': "that's why we use it.", 'start': 22072.407, 'duration': 1.802}, {'end': 22083.155, 'text': "so basically, this column, this column a, is a series, column b is a series and this entire thing is a banda's data frame.", 'start': 22074.209, 'duration': 8.946}], 'summary': 'Using dictionaries to represent fruit count data, resulting in 10 apples, 12 mangoes, and 13 bananas in a dataframe.', 'duration': 28.796, 'max_score': 22054.359, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo22054359.jpg'}, {'end': 22218.528, 'src': 'embed', 'start': 22190.669, 'weight': 3, 'content': [{'end': 22194.852, 'text': 'the chat window is really small, so if i missed something, just let me know.', 'start': 22190.669, 'duration': 4.183}, {'end': 22196.473, 'text': 'okay, i can.', 'start': 22194.852, 'duration': 1.621}, {'end': 22200.496, 'text': 'i can repeat that also.', 'start': 22196.473, 'duration': 4.023}, {'end': 22204.119, 'text': 'abhishek, can we rename the index of rows?', 'start': 22200.496, 'duration': 3.623}, {'end': 22206.701, 'text': 'that is, by default is coming like zero one.', 'start': 22204.119, 'duration': 2.582}, {'end': 22207.301, 'text': 'so can we?', 'start': 22206.701, 'duration': 0.6}, {'end': 22208.242, 'text': 'it is, can we rename it?', 'start': 22207.301, 'duration': 0.941}, {'end': 22211.464, 'text': "Yes, but that's a no use, right?", 'start': 22209.983, 'duration': 1.481}, {'end': 22212.945, 'text': 'So you will.', 'start': 22211.884, 'duration': 1.061}, {'end': 22218.528, 'text': 'when you are accessing the Python object, you will max out the times.', 'start': 22212.945, 'duration': 5.583}], 'summary': 'Discussion about renaming index of rows in python object.', 'duration': 27.859, 'max_score': 22190.669, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo22190669.jpg'}, {'end': 22337.46, 'src': 'embed', 'start': 22311.974, 'weight': 4, 'content': [{'end': 22319.237, 'text': 'you can pass it in the index index parameter of the df dot rename thing and there it will be changing.', 'start': 22311.974, 'duration': 7.263}, {'end': 22321.997, 'text': 'it will be changing the rows based on the.', 'start': 22319.237, 'duration': 2.76}, {'end': 22332.72, 'text': 'it will be changing the row indexes, okay, but if you want to change the columns, then you need to change your pass in index equal to string or int,', 'start': 22321.997, 'duration': 10.723}, {'end': 22333.44, 'text': 'something like that.', 'start': 22332.72, 'duration': 0.72}, {'end': 22336.559, 'text': 'So this index will contain this index.', 'start': 22334.258, 'duration': 2.301}, {'end': 22337.46, 'text': 'The row indexes.', 'start': 22336.619, 'duration': 0.841}], 'summary': 'Changing row indexes using df.rename with index parameter.', 'duration': 25.486, 'max_score': 22311.974, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo22311974.jpg'}, {'end': 23817.691, 'src': 'embed', 'start': 23790.822, 'weight': 6, 'content': [{'end': 23796.685, 'text': 'okay, so here we want to see correlation of one column to all the other columns.', 'start': 23790.822, 'duration': 5.863}, {'end': 23798.466, 'text': 'that is present in the data set.', 'start': 23796.685, 'duration': 1.781}, {'end': 23801.008, 'text': "so that's why we have passed in all the names.", 'start': 23798.466, 'duration': 2.542}, {'end': 23804.649, 'text': 'Okay, all the names within the second bracket.', 'start': 23801.648, 'duration': 3.001}, {'end': 23807.049, 'text': 'So that is basically slicing, right? That is slicing.', 'start': 23804.669, 'duration': 2.38}, {'end': 23810.41, 'text': 'We have passed in a list of columns inside the data frame.', 'start': 23807.469, 'duration': 2.941}, {'end': 23817.691, 'text': 'So it will provide you all the values of the columns and it will calculate the correlation using the CORR function.', 'start': 23810.79, 'duration': 6.901}], 'summary': 'Calculating correlation of one column with all others in the dataset using the corr function.', 'duration': 26.869, 'max_score': 23790.822, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo23790822.jpg'}], 'start': 21215.258, 'title': 'Pandas for data analysis', 'summary': 'Discusses the superiority of pandas over numpy for data analysis, its suitability for tabulated, matrix, and time series data, basics of creating data frames, dataframe manipulation, understanding data joins, and data analysis techniques in pandas.', 'chapters': [{'end': 21294.663, 'start': 21215.258, 'title': 'Pandas vs numpy for data analysis', 'summary': "Discusses the superiority of pandas over numpy for data analysis, citing reasons such as pandas' ability to handle string arrays and multidimensional arrays with named columns and rows, outperforming numpy for 500k+ data points, and offering more flexibility with labeled index for rows and columns.", 'duration': 79.405, 'highlights': ['Pandas perform better than NumPy for 500k and more, offering superior performance for larger datasets, as demonstrated with time and space comparisons.', 'Pandas allows for more flexibility with labeled index for rows and columns, enabling the definition of custom level index, providing a distinct advantage over NumPy.', 'Pandas is essential for handling real-world cases where NumPy may not be suitable, particularly in scenarios requiring processing of string arrays or multidimensional arrays with named columns and rows.']}, {'end': 21814.019, 'start': 21294.663, 'title': 'Pandas data analysis', 'summary': 'Introduces the pandas module, highlighting its suitability for tabulated, matrix, and time series data. it focuses on the creation and manipulation of series objects and data frames, emphasizing their use in handling one-dimensional and multi-dimensional data, as well as their flexibility in handling different data types. the chapter also emphasizes the labeled access, arithmetic operations, and the ability to perform meaningful statistical analysis using the pandas data frame.', 'duration': 519.356, 'highlights': ['Pandas module is suitable for tabulated, matrix, and time series data Pandas module is suitable for tabulated, matrix, and time series data, offering flexibility in handling different data types.', 'Creation and manipulation of series objects and data frames The chapter focuses on the creation and manipulation of series objects and data frames, emphasizing their use in handling one-dimensional and multi-dimensional data.', 'Flexibility in handling different data types within data frames Data frames in pandas offer the flexibility to handle different data types within columns, allowing for the inclusion of strings, integers, and floats.', 'Ability to perform meaningful statistical analysis using the pandas data frame The pandas data frame enables users to perform meaningful statistical analysis, including labeled access, arithmetic operations, and obtaining statistics on the data.']}, {'end': 22122.827, 'start': 21814.019, 'title': 'Pandas data frame basics', 'summary': 'Covers the basics of creating data frames in pandas, including converting lists, series, numpy arrays, and dictionaries into data frames, and the considerations and errors when mapping data to columns and rows.', 'duration': 308.808, 'highlights': ['Data frames in Pandas are built on top of numpy arrays, allowing for the conversion of various data structures such as lists, series, numpy arrays, and dictionaries into data frames. Pandas data frames are built on numpy arrays, enabling the conversion of various data structures into data frames.', "When mapping data to columns and rows in a data frame, it's important to ensure that the size of the data matches the size of the columns to avoid errors such as 'value error' and 'null' values. Mapping data to columns and rows in a data frame requires ensuring the size of the data matches the size of the columns to avoid errors and null values.", 'Individual columns in a data frame are series objects, and renaming index names can be done to enhance clarity and understanding of the data. Columns in a data frame are series objects, and renaming index names can enhance clarity and understanding of the data.']}, {'end': 22933.085, 'start': 22122.827, 'title': 'Pandas dataframe manipulation', 'summary': 'Covers creating data frames from different types, renaming row and column indexes, and performing inner, outer, left, and right joins using pandas, with an emphasis on concat and merge methods for joining data frames.', 'duration': 810.258, 'highlights': ['Creating data frames from different types Data frames can be created from a variety of data types, such as dictionaries and numpy arrays.', "Renaming row and column indexes Row and column indexes can be renamed by passing dictionaries or lists to the 'rename' method of the data frame, with the option to use 'inplace' parameter for replacement.", "Performing inner, outer, left, and right joins using Pandas Pandas allows for performing inner, outer, left, and right joins between data frames using methods like 'concat' and 'merge', with the ability to specify the axis for joining rows or columns.", "Difference between concat and merge methods The 'concat' method merges data frames based on concatenation principles, while the 'merge' method works similarly to SQL joins, producing outputs that resemble SQL join results."]}, {'end': 23836.651, 'start': 22933.085, 'title': 'Understanding data joins and data analysis in pandas', 'summary': 'Covers the fundamentals of data joins in pandas, including inner joins, merge, and concat, and then delves into data analysis techniques such as data cleansing, basic statistics, and correlation between columns using pandas, offering insights into real-life data sets and practical applications.', 'duration': 903.566, 'highlights': ['Explaining inner join in data joins The chapter explains the concept of inner join, demonstrating how it compares values of two columns and outputs only the matching values, providing a clear example with quantitative data.', 'Introduction to data cleansing techniques in Pandas The transcript introduces data cleansing techniques, such as filling null values with mean and dropping unnecessary columns, offering practical guidance on maintaining data integrity.', "Utilizing Pandas functions for data analysis The chapter demonstrates how to use Pandas functions like 'describe' to obtain basic statistics, such as count, mean, and standard deviation, providing a comprehensive overview of data analysis in Pandas."]}], 'duration': 2621.393, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo21215258.jpg', 'highlights': ['Pandas perform better than NumPy for 500k and more, offering superior performance for larger datasets, as demonstrated with time and space comparisons.', 'Pandas allows for more flexibility with labeled index for rows and columns, enabling the definition of custom level index, providing a distinct advantage over NumPy.', 'Pandas is essential for handling real-world cases where NumPy may not be suitable, particularly in scenarios requiring processing of string arrays or multidimensional arrays with named columns and rows.', 'Pandas module is suitable for tabulated, matrix, and time series data Pandas module is suitable for tabulated, matrix, and time series data, offering flexibility in handling different data types.', 'Ability to perform meaningful statistical analysis using the pandas data frame The pandas data frame enables users to perform meaningful statistical analysis, including labeled access, arithmetic operations, and obtaining statistics on the data.', 'Data frames in Pandas are built on top of numpy arrays, allowing for the conversion of various data structures such as lists, series, numpy arrays, and dictionaries into data frames. Pandas data frames are built on numpy arrays, enabling the conversion of various data structures into data frames.', "When mapping data to columns and rows in a data frame, it's important to ensure that the size of the data matches the size of the columns to avoid errors such as 'value error' and 'null' values. Mapping data to columns and rows in a data frame requires ensuring the size of the data matches the size of the columns to avoid errors and null values.", 'Creating data frames from different types Data frames can be created from a variety of data types, such as dictionaries and numpy arrays.', "Performing inner, outer, left, and right joins using Pandas Pandas allows for performing inner, outer, left, and right joins between data frames using methods like 'concat' and 'merge', with the ability to specify the axis for joining rows or columns.", 'Introduction to data cleansing techniques in Pandas The transcript introduces data cleansing techniques, such as filling null values with mean and dropping unnecessary columns, offering practical guidance on maintaining data integrity.']}, {'end': 26149.938, 'segs': [{'end': 24153.23, 'src': 'embed', 'start': 24131.06, 'weight': 2, 'content': [{'end': 24139.184, 'text': 'uh, things will be integers and then if you, when i, when i tell integers, it will be apply, it will be applied to all the uh,', 'start': 24131.06, 'duration': 8.124}, {'end': 24143.306, 'text': 'like those indexing and slicing, slicing techniques that we have seen till now.', 'start': 24139.184, 'duration': 4.122}, {'end': 24144.846, 'text': 'okay, so that is what i lock.', 'start': 24143.306, 'duration': 1.54}, {'end': 24151.789, 'text': 'so if you check the outputs here, six colon, four colon, that means from sixth row onwards and four column means fourth column onwards.', 'start': 24144.846, 'duration': 6.943}, {'end': 24153.23, 'text': 'so like that it will match.', 'start': 24151.789, 'duration': 1.441}], 'summary': 'Integers used for indexing and slicing techniques, e.g. 6: and 4: for rows and columns.', 'duration': 22.17, 'max_score': 24131.06, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo24131060.jpg'}, {'end': 24604.814, 'src': 'embed', 'start': 24579.277, 'weight': 0, 'content': [{'end': 24586.304, 'text': 'uh, thought of next one is again another line plot with the displacement, so you can see the displacement values in there,', 'start': 24579.277, 'duration': 7.027}, {'end': 24591.545, 'text': 'similarly to the line plot okay, similar to line plot for hp curve Okay.', 'start': 24586.304, 'duration': 5.241}, {'end': 24595.148, 'text': 'So next one is to merge two curves in the same plot.', 'start': 24591.565, 'duration': 3.583}, {'end': 24599.11, 'text': 'So what we have done, we have taken two Y variables, HP and displacement.', 'start': 24595.428, 'duration': 3.682}, {'end': 24604.814, 'text': 'We have placed both in there and see this kind of a beautiful curve is being displayed over here.', 'start': 24599.631, 'duration': 5.183}], 'summary': 'Demonstrated merging two curves in a plot with hp and displacement variables.', 'duration': 25.537, 'max_score': 24579.277, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo24579277.jpg'}, {'end': 25091.129, 'src': 'embed', 'start': 25062.199, 'weight': 4, 'content': [{'end': 25066.702, 'text': 'Right And we have used the alias just to have ease of reference.', 'start': 25062.199, 'duration': 4.503}, {'end': 25070.417, 'text': "Right So that we don't need to write the entire thing in there.", 'start': 25066.802, 'duration': 3.615}, {'end': 25072.258, 'text': "So that's how it is.", 'start': 25070.777, 'duration': 1.481}, {'end': 25075.1, 'text': "Okay So that's where this matplotlib is.", 'start': 25072.678, 'duration': 2.422}, {'end': 25076.961, 'text': "That's how easy matplotlib is.", 'start': 25075.5, 'duration': 1.461}, {'end': 25085.666, 'text': 'Next what we are going to see is what are the different types of plot that we have.', 'start': 25081.944, 'duration': 3.722}, {'end': 25088.367, 'text': 'Before that let me come to this inline thing.', 'start': 25086.226, 'duration': 2.141}, {'end': 25091.129, 'text': 'So this inline function.', 'start': 25089.608, 'duration': 1.521}], 'summary': "Introduction to using aliases for ease of reference and discussing matplotlib's simplicity and inline function.", 'duration': 28.93, 'max_score': 25062.199, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo25062199.jpg'}], 'start': 23836.651, 'title': 'Data manipulation, visualization, and correlation analysis', 'summary': 'Covers understanding correlation matrix, pandas data manipulation and visualization, basics of pandas and matplotlib, and matplotlib library for graph display and customization. it explains the significance of correlation matrix in identifying feature relationships and provides examples of predicting housing values. additionally, it includes details on data manipulation, visualization using pandas, different types of plots available in matplotlib, and customization options for visually appealing graphs and charts.', 'chapters': [{'end': 24034.904, 'start': 23836.651, 'title': 'Understanding correlation matrix', 'summary': 'Explains the concept of correlation matrix and its significance in identifying the relationship between features, with an example of predicting housing values based on square feet of area and garden space, and the importance of removing unnecessary columns with low correlation.', 'duration': 198.253, 'highlights': ['Correlation matrix shows the correlation between features, with values ranging from -1 to +1, indicating inverse or strong positive relationships, such as miles per gallon and number of cylinders being inversely proportional, and the importance of this understanding in predicting housing values. Correlation matrix displays values from -1 to +1, indicating the nature of the relationship between features, and this understanding is crucial for predicting housing values.', 'The example of predicting housing values illustrates the impact of features like garden space on price, and the significance of removing unnecessary columns with low correlation, such as gate size and wall paint, in order to make accurate estimations. The example of predicting housing values highlights the influence of features like garden space on price, and emphasizes the importance of removing irrelevant columns with low correlation, such as gate size and wall paint, for accurate estimations.', 'Columns with a correlation of less than 5% can be considered for removal, with the recommendation to remove those with very small correlation, indicated by values in the range of 0.00, to ensure accurate estimations. Columns with a correlation of less than 5% or very small values in the range of 0.00 are recommended for removal to enhance the accuracy of estimations.']}, {'end': 24761.826, 'start': 24034.984, 'title': 'Pandas data manipulation and visualization', 'summary': 'Covers data manipulation and visualization using pandas, including changing null counts to float, accessing data, applying lambda functions, sorting values, filtering records, and plotting different types of charts using matplotlib.', 'duration': 726.842, 'highlights': ['Changing null counts to float and filling in the qsec column resulted in having 32 non-null values, ensuring all columns have the same number of values.', 'Accessing data using iLoc, which is indexed location, and slicing by level, which allows for selecting specific rows and columns based on labels and indices.', "Applying lambda functions to derive new columns, such as doubling the values in the 'am' column, and sorting the data frame based on specific columns using 'sort_values'.", 'Filtering records based on conditions, including filtering by cylinder count and horsepower, and visualizing data through line plots, stack plots, area plots, and bar charts using matplotlib and pandas objects.']}, {'end': 25175.214, 'start': 24761.827, 'title': 'Pandas and matplotlib basics', 'summary': 'Covers basics of pandas and matplotlib, including creating dataframes, joining arrays, renaming columns, and using matplotlib for data visualization. it also explains the importance of choosing matplotlib for data visualization and the different types of plots available, with a focus on line plots.', 'duration': 413.387, 'highlights': ['The chapter explains the basics of Pandas and Matplotlib, including creating dataframes, joining arrays, and renaming columns, with a focus on data visualization using matplotlib. It also highlights the importance of matplotlib for data visualization and the different types of plots available, with a focus on line plots. The chapter provides practical examples of creating dataframes, joining arrays, renaming columns, and using matplotlib for data visualization. It also emphasizes the importance of choosing matplotlib for data visualization and the different types of plots available, with a focus on line plots. The chapter also discusses the inline function in Jupyter notebook and its impact on data visualization using matplotlib.', 'The chapter provides practical examples of creating dataframes, joining arrays, renaming columns, and using matplotlib for data visualization. It also emphasizes the importance of choosing matplotlib for data visualization and the different types of plots available, with a focus on line plots. The chapter explains the basics of Pandas and Matplotlib, including creating dataframes, joining arrays, and renaming columns, with a focus on data visualization using matplotlib.']}, {'end': 25431.081, 'start': 25175.214, 'title': 'Matplotlib library: graph display and customization', 'summary': "Covers the use of 'matplotlib inline' command to display graphs in the same window, the different types of plots available, and the powerful customization options in matplotlib for creating visually appealing graphs and charts, along with real-world use cases.", 'duration': 255.867, 'highlights': ["The 'matplotlib inline' command is used to display graphs in the same window, but separate windows can also be used. The 'matplotlib inline' command is the standard way to display graphs in the same window, but separate windows can also be used if desired.", 'The lecture emphasizes understanding why and what plot to use, where to use it, and how to write customizations for plots. The lecture emphasizes understanding the purpose of different types of plots, their appropriate usage, and the process of writing customizations for plots.', 'The speaker shares a real-world use case of using Python to create graphs and charts for reporting to upper management. The speaker shares a real-world example of using Python to create graphs and charts for reporting to upper management, highlighting the practical application of matplotlib in a business context.', 'Matplotlib offers extensive customization options including changing background and foreground colors, individual bar color selection, and access to individual bar objects for customizations. Matplotlib offers extensive customization options such as changing background and foreground colors, selecting individual bar colors, and accessing individual bar objects for customizations.', 'The speaker encourages experimentation and understanding of the core concepts of matplotlib for building up the knowledge of graph customizations. The speaker encourages experimentation and understanding of the core concepts of matplotlib for building up the knowledge of graph customizations, with the emphasis on starting with the basics and gradually progressing to more advanced customizations.']}, {'end': 26149.938, 'start': 25431.081, 'title': 'Matplotlib graph types and customization', 'summary': 'Introduces the usage of line plots, bar plots, scatter plots, histograms, and image plots in matplotlib, providing insights into their applications, differences, and customization options. it also demonstrates basic customization techniques such as figure size, line width, line style, marker, and color in creating plots.', 'duration': 718.857, 'highlights': ['The chapter covers the usage of line plots, bar plots, scatter plots, histograms, and image plots in matplotlib. The chapter discusses the various types of plots available in matplotlib, such as line plots, bar plots, scatter plots, histograms, and image plots.', 'It explains the differences between line plots, bar plots, and scatter plots, and their respective applications in visualizing data distributions and trend lines. The chapter elaborates on the distinctions between line plots, bar plots, and scatter plots, highlighting their specific applications in visualizing data distributions, trend lines, and linear equations.', 'The importance of histograms for image processing and their usage in analyzing data spread and frequency of data points is discussed. The chapter emphasizes the significance of histograms in image processing and its role in analyzing data spread and the frequency of data points.', 'Basic customization techniques such as figure size, line width, line style, marker, and color are demonstrated for creating plots. The chapter provides insights into basic customization techniques, including figure size, line width, line style, marker, and color, to enhance the visual representation of plots.']}], 'duration': 2313.287, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo23836651.jpg', 'highlights': ['The example of predicting housing values illustrates the impact of features like garden space on price, and the significance of removing unnecessary columns with low correlation, such as gate size and wall paint, in order to make accurate estimations.', 'Columns with a correlation of less than 5% can be considered for removal, with the recommendation to remove those with very small correlation, indicated by values in the range of 0.00, to ensure accurate estimations.', 'The chapter discusses the various types of plots available in matplotlib, such as line plots, bar plots, scatter plots, histograms, and image plots.', 'The chapter explains the basics of Pandas and Matplotlib, including creating dataframes, joining arrays, and renaming columns, with a focus on data visualization using matplotlib.', 'Matplotlib offers extensive customization options such as changing background and foreground colors, selecting individual bar colors, and accessing individual bar objects for customizations.']}, {'end': 28762.135, 'segs': [{'end': 27502.969, 'src': 'embed', 'start': 27474.281, 'weight': 0, 'content': [{'end': 27477.804, 'text': "right, you don't know the counts you want to find out without writing a code.", 'start': 27474.281, 'duration': 3.523}, {'end': 27478.104, 'text': 'you can.', 'start': 27477.804, 'duration': 0.3}, {'end': 27488.313, 'text': 'if you plot a histogram, you will have the count, so that i told when i covered the theory part of it, right, okay, next is customizing histograms.', 'start': 27478.104, 'duration': 10.209}, {'end': 27491.316, 'text': 'again. we have provided this bin numbers.', 'start': 27488.313, 'duration': 3.003}, {'end': 27495.064, 'text': 'h color, we have provided as black and color.', 'start': 27492.282, 'duration': 2.782}, {'end': 27497.225, 'text': 'we have provided this funky red color.', 'start': 27495.064, 'duration': 2.161}, {'end': 27502.969, 'text': 'okay, title x level, viable grid suffix already, you know this, right.', 'start': 27497.225, 'duration': 5.744}], 'summary': 'Customizing histograms with bin numbers and colors for data visualization.', 'duration': 28.688, 'max_score': 27474.281, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo27474281.jpg'}, {'end': 28435.47, 'src': 'embed', 'start': 28408.787, 'weight': 1, 'content': [{'end': 28414.731, 'text': 'so how i mean i am trying to explain when you use it and how you use it.', 'start': 28408.787, 'duration': 5.944}, {'end': 28418.173, 'text': "so let's say you have built a model without seeing anything.", 'start': 28414.731, 'duration': 3.442}, {'end': 28419.514, 'text': 'you have put in some features.', 'start': 28418.173, 'duration': 1.341}, {'end': 28421.175, 'text': 'you have taken into.', 'start': 28419.514, 'duration': 1.661}, {'end': 28426.018, 'text': 'you have assigned the features into the i mean weight to the features.', 'start': 28421.175, 'duration': 4.843}, {'end': 28428.519, 'text': 'so now, how do you validate that?', 'start': 28426.018, 'duration': 2.501}, {'end': 28435.47, 'text': 'so if some feature values are in between lying heavily between 0 and 20 or 30,', 'start': 28428.519, 'duration': 6.951}], 'summary': 'Explaining model validation with feature weight assignment and value range between 0 and 30', 'duration': 26.683, 'max_score': 28408.787, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo28408787.jpg'}], 'start': 26149.938, 'title': 'Data visualization techniques', 'summary': 'Covers customizing line plots and creating subplots, bar and scatter plots, various data visualization techniques, understanding box plots and quartiles, and box and violin plots for data analysis, with emphasis on matplotlib in python and visually appealing plots.', 'chapters': [{'end': 26935.773, 'start': 26149.938, 'title': 'Customizing line plots and creating subplots', 'summary': 'Explains customizing line plots, using color attributes for graphs, understanding alpha for transparency, and creating subplots to display multiple line plots in the same figure.', 'duration': 785.835, 'highlights': ['Explanation of how to use color attributes for graphs, including using hex values or mnemonics, and demonstrating the functionality with examples. The speaker explains how to use color attributes for graphs, including the option to use hex values or mnemonics, and provides examples to demonstrate the functionality.', 'Understanding alpha for transparency in graphs, with practical examples showcasing the impact of alpha values on the brightness and transparency of the plotted elements. The chapter delves into understanding alpha for transparency in graphs, demonstrating its impact with practical examples showcasing the effect of alpha values on the brightness and transparency of the plotted elements.', 'Demonstration of creating subplots to display multiple line plots in the same figure, with detailed explanation of the parameters for customizing the arrangement of subplots. The chapter provides a detailed demonstration of creating subplots to display multiple line plots in the same figure, along with an explanation of the parameters for customizing the arrangement of subplots.']}, {'end': 27201.005, 'start': 26935.773, 'title': 'Customizing bar and scatter plots', 'summary': 'Covers customization of bar and scatter plots including color, size, and marker options for creating visually appealing and differentiated plots, with emphasis on the basic techniques and functionality.', 'duration': 265.232, 'highlights': ['The chapter covers customization of bar and scatter plots, including color, size, and marker options for creating visually appealing plots (e.g., using the color parameter for bar plots and the c parameter for scatter plots).', "The size of the data points in scatter plots can be customized using the 's' parameter, allowing for variation in the size of the plotted points.", "Differentiation between data points in scatter plots can be achieved using the 'h' colors parameter, which allows for color differentiation and the addition of borders to the data points."]}, {'end': 27652.273, 'start': 27201.005, 'title': 'Data visualization techniques', 'summary': 'Covers various data visualization techniques including saving figures, understanding histograms, and customizing histograms using matplotlib in python, emphasizing the importance of displaying data graphically for better understanding and insights.', 'duration': 451.268, 'highlights': ['The chapter introduces techniques for saving figures using plt.savefig and demonstrates the process of saving a scatter plot as an image, emphasizing the practical application of the technique for sharing and using the generated images. plt.savefig, scatterplot.png', 'The detailed explanation of histograms includes the concept of bins, understanding the frequency of data points within specific intervals, and the practical application of histograms for visualizing counts in a data set. bins, frequency of data points', 'The importance of customizing histograms using attributes such as h color and color is emphasized, highlighting the ability to tailor the visual representation of data based on specific requirements. customizing histograms, h color, color', 'The significance of data visualization techniques is underscored, emphasizing the challenge of understanding and deriving insights from large real-world data sets, and the role of graphical representation in simplifying data comprehension. real-world data sets, insights, graphical representation']}, {'end': 28089.541, 'start': 27652.273, 'title': 'Understanding box plots and quartiles', 'summary': 'Explains the concept of box plots and quartiles, detailing the calculation of quartiles, mean, and interquartile range, and their significance in visualizing the distribution and concentration of data points, emphasizing the use of box plots to represent data sets.', 'duration': 437.268, 'highlights': ['The chapter explains the calculation of quartiles, mean, and interquartile range, emphasizing their significance in visualizing the distribution and concentration of data points, particularly through the use of box plots.', 'The mean, also referred to as the second quartile (Q2), is calculated as the middle point of the data set, and for even numbers of values, the middle two values are considered to find the mean.', 'The chapter illustrates the use of box plots to represent the quartile information of a data set, providing insights into the concentration of data points and the frequency of data within specific ranges.', 'The process of combining separate lists of total order and discount into a single list for plotting using the box plot command is outlined, with emphasis on the significance of the orange dots representing the means in the box plot visualization.']}, {'end': 28762.135, 'start': 28089.541, 'title': 'Understanding box and violin plots for data analysis', 'summary': "Explains the relevance and usage of box plots and violin plots for data analysis, emphasizing the interpretation of box plot's interquartile range and the probability density function of the violin plot, as well as the application of these plots in machine learning data sets.", 'duration': 672.594, 'highlights': ['The box plot displays data distribution and interquartile range, aiding in the analysis of the quartiles and identification of outliers, with the height representing the interquartile range and its relevance in frequency of the data set. The height of the box plot represents the interquartile range (IQR), which indicates the distance between the first and third quartiles. This aids in analyzing data distribution, quartiles, and identifying outliers.', "The violin plot illustrates the probability density function of a data set, providing insights into the likelihood of specific values occurring within the data set's range, and its application in machine learning for understanding feature distributions and weights. The shaded area outside the middle line of the violin plot represents the probability density function, offering insights into the likelihood of values occurring within the data set's range. It can be applied in machine learning to understand feature distributions and their weights.", 'The area plot visually represents the area covered by a curve in a line plot, offering a visual demonstration of the covered area by the curve. The area plot visually demonstrates the area covered by a curve in a line plot, providing a visual representation of the covered area.', 'The quiver and stream plots are primarily utilized in physics for vector analysis, with the quiver plot representing directed vectors and the stream plot serving the needs of physicists and electrical engineers for analyzing vector directions and current flow. The quiver plot represents directed vectors often used in physics for magnetic field analytics, while the stream plot is utilized by physicists and electrical engineers for analyzing vector directions and current flow.']}], 'duration': 2612.197, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo26149938.jpg', 'highlights': ['The chapter covers customization of bar and scatter plots, including color, size, and marker options for creating visually appealing plots (e.g., using the color parameter for bar plots and the c parameter for scatter plots).', 'The box plot displays data distribution and interquartile range, aiding in the analysis of the quartiles and identification of outliers, with the height representing the interquartile range and its relevance in frequency of the data set.', 'The chapter explains the calculation of quartiles, mean, and interquartile range, emphasizing their significance in visualizing the distribution and concentration of data points, particularly through the use of box plots.', "The violin plot illustrates the probability density function of a data set, providing insights into the likelihood of specific values occurring within the data set's range, and its application in machine learning for understanding feature distributions and weights.", 'The area plot visually represents the area covered by a curve in a line plot, offering a visual demonstration of the covered area by the curve.', 'The quiver and stream plots are primarily utilized in physics for vector analysis, with the quiver plot representing directed vectors and the stream plot serving the needs of physicists and electrical engineers for analyzing vector directions and current flow.', 'The chapter introduces techniques for saving figures using plt.savefig and demonstrates the process of saving a scatter plot as an image, emphasizing the practical application of the technique for sharing and using the generated images.', 'The significance of data visualization techniques is underscored, emphasizing the challenge of understanding and deriving insights from large real-world data sets, and the role of graphical representation in simplifying data comprehension.', "The size of the data points in scatter plots can be customized using the 's' parameter, allowing for variation in the size of the plotted points.", 'The mean, also referred to as the second quartile (Q2), is calculated as the middle point of the data set, and for even numbers of values, the middle two values are considered to find the mean.']}, {'end': 32378.689, 'segs': [{'end': 28907.265, 'src': 'embed', 'start': 28876.285, 'weight': 7, 'content': [{'end': 28882.287, 'text': 'so if the data are very closely stacked, that means x and y values are very closely related.', 'start': 28876.285, 'duration': 6.002}, {'end': 28883.788, 'text': 'so we will have a high correlation.', 'start': 28882.287, 'duration': 1.501}, {'end': 28891.575, 'text': 'if, if, as they are, uh, if they are widespread, then we will have a low correlation, right, because they they will be independent.', 'start': 28883.788, 'duration': 7.787}, {'end': 28899.96, 'text': 'if you see very high, very high, high, uh, high, what, uh, sorry.', 'start': 28891.575, 'duration': 8.385}, {'end': 28907.265, 'text': 'so if you have a very high difference between two data points, uh, so that, uh, that means if the difference is more,', 'start': 28899.96, 'duration': 7.305}], 'summary': 'Closely stacked data indicates high correlation, while widespread data indicates low correlation.', 'duration': 30.98, 'max_score': 28876.285, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo28876285.jpg'}, {'end': 29082.115, 'src': 'embed', 'start': 29058.428, 'weight': 18, 'content': [{'end': 29065.057, 'text': 'you can, you have image processing library called pillow and inside python, okay, you have pillow library in python.', 'start': 29058.428, 'duration': 6.629}, {'end': 29065.838, 'text': 'you can install it.', 'start': 29065.057, 'duration': 0.781}, {'end': 29066.599, 'text': 'no need to do it.', 'start': 29065.838, 'duration': 0.761}, {'end': 29068.201, 'text': 'okay, no need to do it or remember it.', 'start': 29066.599, 'duration': 1.602}, {'end': 29070.424, 'text': "this, this won't be used.", 'start': 29068.201, 'duration': 2.223}, {'end': 29075.671, 'text': 'so using pillow library, you can use this image function and you can open a image.', 'start': 29070.424, 'duration': 5.247}, {'end': 29078.153, 'text': 'all images are what arrays?', 'start': 29076.472, 'duration': 1.681}, {'end': 29079.774, 'text': 'right, they are arrays of pixels.', 'start': 29078.153, 'duration': 1.621}, {'end': 29082.115, 'text': 'so you can convert it to a numpy array.', 'start': 29079.774, 'duration': 2.341}], 'summary': 'Pillow library in python allows image processing, including opening images as arrays and converting them to numpy arrays.', 'duration': 23.687, 'max_score': 29058.428, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo29058428.jpg'}, {'end': 29526.245, 'src': 'embed', 'start': 29495.997, 'weight': 15, 'content': [{'end': 29497.717, 'text': 'so donut chart is the same thing.', 'start': 29495.997, 'duration': 1.72}, {'end': 29502.579, 'text': 'uh, just the difference is it shows the area instead of showing the percentage.', 'start': 29497.717, 'duration': 4.862}, {'end': 29503.719, 'text': 'it shows the area.', 'start': 29502.579, 'duration': 1.14}, {'end': 29505.36, 'text': 'okay, for that, what we do?', 'start': 29503.719, 'duration': 1.641}, {'end': 29508.581, 'text': 'we create two pie charts and we combine those.', 'start': 29505.36, 'duration': 3.221}, {'end': 29511.101, 'text': 'okay. so see, here we create one pie chart.', 'start': 29508.581, 'duration': 2.52}, {'end': 29516.063, 'text': 'here, this pie chart, if i show you one by one.', 'start': 29511.101, 'duration': 4.962}, {'end': 29521.923, 'text': 'So this is a pie chart, right?', 'start': 29520.222, 'duration': 1.701}, {'end': 29523.884, 'text': "We don't have the numbers mentioned.", 'start': 29522.343, 'duration': 1.541}, {'end': 29526.245, 'text': "So we haven't mentioned not opacity.", 'start': 29524.084, 'duration': 2.161}], 'summary': 'The transcript discusses creating a donut chart by combining two pie charts to show areas instead of percentages.', 'duration': 30.248, 'max_score': 29495.997, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo29495997.jpg'}, {'end': 29684.963, 'src': 'embed', 'start': 29652.381, 'weight': 10, 'content': [{'end': 29653.742, 'text': 'So this part is green.', 'start': 29652.381, 'duration': 1.361}, {'end': 29658.143, 'text': 'So next I am changing it.', 'start': 29656.702, 'duration': 1.441}, {'end': 29660.463, 'text': 'This part is pink.', 'start': 29659.463, 'duration': 1}, {'end': 29662.244, 'text': "Let's say like that.", 'start': 29661.604, 'duration': 0.64}, {'end': 29666.205, 'text': 'Okay And this part is orange.', 'start': 29662.264, 'duration': 3.941}, {'end': 29668.214, 'text': 'This is filled up.', 'start': 29667.574, 'duration': 0.64}, {'end': 29669.595, 'text': 'This is entirely filled up.', 'start': 29668.575, 'duration': 1.02}, {'end': 29671.256, 'text': 'I am not going to fill it up.', 'start': 29669.735, 'duration': 1.521}, {'end': 29677.419, 'text': 'So what you can do, you want to show only this much.', 'start': 29671.916, 'duration': 5.503}, {'end': 29679.56, 'text': "Let's say only this much you want to show.", 'start': 29677.839, 'duration': 1.721}, {'end': 29681.501, 'text': 'Oops, sorry.', 'start': 29680.721, 'duration': 0.78}, {'end': 29683.342, 'text': 'Let me change the pin color.', 'start': 29682.342, 'duration': 1}, {'end': 29684.963, 'text': 'It will be easier for you to understand.', 'start': 29683.382, 'duration': 1.581}], 'summary': 'Transcript: describes color changes, filling, and display preferences.', 'duration': 32.582, 'max_score': 29652.381, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo29652381.jpg'}, {'end': 29889.926, 'src': 'embed', 'start': 29860.447, 'weight': 0, 'content': [{'end': 29864.569, 'text': "but this quaver and stream you won't use by donut, you will use often.", 'start': 29860.447, 'duration': 4.122}, {'end': 29870.553, 'text': 'if you have some presentations, you will use this, and without from now on, what i can suggest you?', 'start': 29864.569, 'duration': 5.984}, {'end': 29878.143, 'text': 'if you are working on some data on excel and generating some graph on excel, try to switch to this one right.', 'start': 29870.553, 'duration': 7.59}, {'end': 29881.444, 'text': 'try to switch to this editor like this, like python.', 'start': 29878.143, 'duration': 3.301}, {'end': 29889.926, 'text': 'okay, so just try to switch to python and then you will have nice looking charts, nice looking things, and you will learn this matplotlib library.', 'start': 29881.444, 'duration': 8.482}], 'summary': 'Switch to using python for data presentations and visualization for better results and learning matplotlib library.', 'duration': 29.479, 'max_score': 29860.447, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo29860447.jpg'}, {'end': 30002.231, 'src': 'embed', 'start': 29956.431, 'weight': 12, 'content': [{'end': 29961.215, 'text': "So as the name suggests, ratings.csv contains all users' ratings of the books.", 'start': 29956.431, 'duration': 4.784}, {'end': 29963.677, 'text': 'So there are a total of 980,000 ratings for 10,000 books from 53,424 users.', 'start': 29961.816, 'duration': 1.861}, {'end': 29977.368, 'text': "So the books.csv contains more information on the books such as the author's name, publication year, book ID and so on.", 'start': 29970.325, 'duration': 7.043}, {'end': 29979.989, 'text': 'Then we have the booktags.csv file.', 'start': 29978.088, 'duration': 1.901}, {'end': 29986.571, 'text': 'So this file comprises of all tag IDs users have assigned to the books and the corresponding tag counts.', 'start': 29980.569, 'duration': 6.002}, {'end': 29994.795, 'text': 'So the tag IDs basically denote the categories into which the books fall into and the counts denote the number of books belonging to each category.', 'start': 29987.292, 'duration': 7.503}, {'end': 29997.705, 'text': 'And we have the tags.csv file.', 'start': 29995.823, 'duration': 1.882}, {'end': 30002.231, 'text': 'So this file contains all the tag names corresponding to the tag IDs.', 'start': 29998.306, 'duration': 3.925}], 'summary': '980,000 ratings for 10,000 books from 53,424 users, with additional details on authors, publication year, and tags.', 'duration': 45.8, 'max_score': 29956.431, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo29956431.jpg'}, {'end': 30403.768, 'src': 'embed', 'start': 30377.16, 'weight': 13, 'content': [{'end': 30382.003, 'text': "So I have given ratings over here and I'm grouping this ratings with respect to user ID.", 'start': 30377.16, 'duration': 4.843}, {'end': 30391.251, 'text': 'After which I am using the mutate function and over here again I am adding a new column, and that new column would be ratings given,', 'start': 30382.604, 'duration': 8.647}, {'end': 30396.395, 'text': 'and I will get that ratings given column with the help of this n function from the deployer package.', 'start': 30391.251, 'duration': 5.144}, {'end': 30403.768, 'text': 'So this n function from the deployer package would basically give me the number of ratings given by each user.', 'start': 30396.995, 'duration': 6.773}], 'summary': "Using mutate function to add 'ratings given' column based on user id, derived from deployer package's n function.", 'duration': 26.608, 'max_score': 30377.16, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo30377160.jpg'}, {'end': 30923.951, 'src': 'embed', 'start': 30891.821, 'weight': 1, 'content': [{'end': 30897.732, 'text': 'So from this graph we can basically infer that there is not even one case where a book was rated more than 10 times.', 'start': 30891.821, 'duration': 5.911}, {'end': 30900.518, 'text': "So let's have a glance at this bar over here.", 'start': 30898.837, 'duration': 1.681}, {'end': 30908.823, 'text': 'So this tells us that there are more than 2500 instances where a single book was rated only by one user.', 'start': 30900.698, 'duration': 8.125}, {'end': 30915.767, 'text': 'So this is for those instances where a single book was rated by two users or in other words a single book was rated two times.', 'start': 30909.423, 'duration': 6.344}, {'end': 30923.951, 'text': 'This is for those instances which tells us that a single book was rated by three users, or in other words, a single book was rated three times,', 'start': 30916.367, 'duration': 7.584}], 'summary': 'No book was rated more than 10 times. over 2500 books were rated by only one user.', 'duration': 32.13, 'max_score': 30891.821, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo30891821.jpg'}, {'end': 31232.136, 'src': 'embed', 'start': 31204.454, 'weight': 3, 'content': [{'end': 31208.531, 'text': 'View of book info.', 'start': 31204.454, 'duration': 4.077}, {'end': 31211.672, 'text': 'right, so guys, this is the tag id.', 'start': 31208.531, 'duration': 3.141}, {'end': 31220.353, 'text': 'this is the count of the tag id, that is the number of times this genre is present, and this column gives us the total count of all of the genres.', 'start': 31211.672, 'duration': 8.681}, {'end': 31226.255, 'text': 'this gives us the percentage of the genre, and this is the tag name, which is fantasy.', 'start': 31220.353, 'duration': 5.902}, {'end': 31228.355, 'text': "so we've got our data set ready.", 'start': 31226.255, 'duration': 2.1}, {'end': 31232.136, 'text': "now we'll go ahead and make a plot on top of this.", 'start': 31228.355, 'duration': 3.781}], 'summary': 'Tag id count indicates the frequency of the fantasy genre in the dataset, ready for plotting.', 'duration': 27.682, 'max_score': 31204.454, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo31204454.jpg'}, {'end': 31818.161, 'src': 'embed', 'start': 31794.013, 'weight': 20, 'content': [{'end': 31803.425, 'text': "So I will store this rating mat into a new object and name that object's name to be rating mat 0.", 'start': 31794.013, 'duration': 9.412}, {'end': 31810.359, 'text': 'Let me again have a glance at the number of dimensions of this dim of rating mat 0.', 'start': 31803.425, 'duration': 6.934}, {'end': 31812.56, 'text': 'So we have the same number of rows and columns.', 'start': 31810.359, 'duration': 2.201}, {'end': 31818.161, 'text': 'So again here the number of rows are 900 and the number of columns are 8, 4, 3, 1.', 'start': 31812.62, 'duration': 5.541}], 'summary': 'Stored rating mat into new object named rating mat 0, with 900 rows and 8, 4, 3, 1 columns.', 'duration': 24.148, 'max_score': 31794.013, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo31794013.jpg'}, {'end': 32058.497, 'src': 'embed', 'start': 32033.1, 'weight': 11, 'content': [{'end': 32041.645, 'text': "First is the training set on which we'd want to build the model and next is the method or the type of the recommender model which we'd want to build.", 'start': 32033.1, 'duration': 8.545}, {'end': 32047.628, 'text': "And since we'd want to build a user-based collaborative filtering model, we'll give in the type to be UBCF.", 'start': 32042.145, 'duration': 5.483}, {'end': 32053.172, 'text': 'So if we wanted an item-based collaborating filtering model, then the method would be IBCF.', 'start': 32048.188, 'duration': 4.984}, {'end': 32058.497, 'text': 'But in our case, since we want user-based collaborative filtering model, so the method would be UBCF.', 'start': 32053.433, 'duration': 5.064}], 'summary': 'Training set for building ubcf model, not ibcf.', 'duration': 25.397, 'max_score': 32033.1, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo32033100.jpg'}], 'start': 28763.316, 'title': 'Plot types, data visualization, and collaborative filtering', 'summary': 'Covers usage of plot types including quiver, stream, area, bar, scatter, histogram, box, and violin plots; visualizing data with python libraries like matplotlib and pillow; pie and donut chart plotting using matplotlib; data cleaning, exploration, and analysis insights; and building a user-based collaborative filtering model with a total of 960,595 entries and recommendations for two different readers.', 'chapters': [{'end': 29032.417, 'start': 28763.316, 'title': 'Plot types and their usage', 'summary': 'Covers the usage of quiver, stream, area, bar, scatter, histogram, box, and violin plots, highlighting their usage and relevance in data analysis.', 'duration': 269.101, 'highlights': ['Bar plot is used for presenting categorical data with rectangular bars, and scatter plot is used to show the relationship between two variables in a 2D plane. Bar plot visually represents categorical data with bars, while scatter plot displays the relationship between two variables in a 2D plane.', 'Boxplot aids in outlier analysis by visually representing the quartiles and identifying data points that lie significantly away from the rest of the data. Boxplot is utilized for outlier analysis by visually representing quartiles and identifying significant outliers in the dataset.', 'Histogram is used to display the frequency distribution across a continuous or discrete variable. Histogram visually displays the frequency distribution across a continuous or discrete variable.']}, {'end': 29351.417, 'start': 29033.092, 'title': 'Visualizing data with python libraries', 'summary': 'Discusses using python libraries like matplotlib and pillow to visualize data, including techniques like image plotting, vector plotting, stream plotting, and pie chart creation, with specific examples and use cases.', 'duration': 318.325, 'highlights': ['The chapter covers image plotting using the pillow library in Python, allowing conversion of images to numpy arrays and display of corresponding pixel values as an image, with specific examples and techniques. Covers image plotting with pillow library, converting images to numpy arrays, displaying pixel values as images.', 'It explains the usage of quiver plot for vector plotting, detailing the process of creating subplots, passing position and direction parameters, and its relevance in physics and engineering applications. Explains usage of quiver plot for vector plotting, creating subplots, passing position and direction parameters, relevance in physics and engineering applications.', 'The chapter also delves into stream plotting for vector algebra and physics applications, discussing its distinction from the vector plot and its relevance in scenarios like wind trend prediction and cyclone analysis. Discusses stream plotting for vector algebra and physics applications, distinction from vector plot, relevance in wind trend prediction and cyclone analysis.', 'It covers the usage and customization of pie charts for displaying percentage or proportional data, explaining its advantage over box plots in scenarios with multiple categories and providing a coding example. Covers usage and customization of pie charts, advantage over box plots in scenarios with multiple categories, provides coding example.']}, {'end': 29903.9, 'start': 29351.417, 'title': 'Matplotlib pie and donut charts', 'summary': 'Explains the process of plotting pie and donut charts using matplotlib, including the use of functions and options such as sizes, labels, levels, auto percentage, shadow, start angle, explode, and the combination of two pie charts to create a donut chart, as well as the demonstration of area chart using stack plot function and the recommendation to switch to python for chart generation.', 'duration': 552.483, 'highlights': ['The chapter explains the process of plotting pie and donut charts using matplotlib, including the use of functions and options such as sizes, labels, levels, auto percentage, shadow, start angle, and explode, with a demonstration of how to combine two pie charts to create a donut chart.', 'The demonstration includes the explanation of the options for pie chart such as sizes, labels, levels, auto percentage, shadow, start angle, explode, and the process of combining two pie charts to create a donut chart.', 'The chapter also demonstrates the use of stack plot function for creating area charts and recommends switching to Python for generating charts, emphasizing the benefits of learning matplotlib library for creating nice looking charts.']}, {'end': 30773.214, 'start': 29903.92, 'title': 'Data cleaning and data exploration', 'summary': 'Covers the data cleaning process, including removing duplicate ratings and users with fewer than three ratings, resulting in a total of 960,595 entries. it then delves into data exploration, where a 2% sample set of 18,832 records is extracted from the entire dataset and a bar plot for the distribution of ratings is created.', 'duration': 869.294, 'highlights': ['The chapter covers the process of removing duplicate ratings and users with fewer than three ratings, resulting in a total of 960,595 entries. The process involves identifying and removing duplicate ratings, with instances such as 4,298 cases of a user rating the same book twice, and then filtering out users who have rated fewer than three books.', 'Data exploration involves extracting a 2% sample set of 18,832 records from the entire dataset and creating a bar plot for the distribution of ratings. A 2% sample set of 18,832 records is extracted from the entire dataset, and a bar plot for the distribution of ratings is created, showcasing the count of different ratings.']}, {'end': 31475.885, 'start': 30774.055, 'title': 'Data analysis insights', 'summary': 'Explores the distribution of ratings, number of ratings per book, percentage distribution of genres, and identifies the top 10 highest rated and most popular books.', 'duration': 701.83, 'highlights': ['The most prevalent genre is fantasy, while the least prevalent is cookbooks. The plot of percentage distribution of genres indicates that fantasy is the most prevalent genre, while cookbooks have the least percentage.', "The top-rated book is 'The Complete Calvin and Hobbes' with an average rating of 4.82. The highest rated book is 'The Complete Calvin and Hobbes' with a remarkable average rating of 4.82.", "The book 'The Hunger Games' is the most popular with the highest ratings count. The most popular book, based on ratings count, is 'The Hunger Games'.", 'The distribution of ratings shows over 6000 cases of 4-star ratings and more than 5000 cases of 5-star ratings. The analysis of distribution of ratings reveals over 6000 cases of 4-star ratings and more than 5000 cases of 5-star ratings.']}, {'end': 32378.689, 'start': 31476.638, 'title': 'Building user-based collaborative filtering model', 'summary': 'Covers the process of building a user-based collaborative filtering model, transforming the data frame into a matrix, and recommending six new books for two different readers, resulting in the extraction of book titles and authors.', 'duration': 902.051, 'highlights': ['The process of building a user-based collaborative filtering model, transforming the data frame into a matrix, and recommending six new books for two different readers. The transcript details the steps involved in building a user-based collaborative filtering model, transforming the data frame into a matrix, and recommending six new books for two different readers.', 'Extracting all of the unique user IDs and unique book IDs and restructuring the data into a matrix format. The speaker explains the process of extracting unique user IDs and book IDs and restructuring the data into a matrix format, with 900 users and 8,431 books.', 'Converting the data frame into a matrix using the as.matrix function and creating train and test sets for model building. The process of converting the data frame into a matrix using the as.matrix function and creating train and test sets for model building is detailed.', 'Using the recommender function to build a user-based collaborative filtering model and predicting the top six book recommendations for two different readers. The speaker demonstrates the use of the recommender function to build a user-based collaborative filtering model and predict the top six book recommendations for two different readers.', 'Extracting and associating the recommended book IDs with their respective titles and authors for user number one and user number five. The process of extracting and associating the recommended book IDs with their respective titles and authors for user number one and user number five is explained.']}], 'duration': 3615.373, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo28763316.jpg', 'highlights': ['Bar plot visually represents categorical data with bars', 'Scatter plot displays the relationship between two variables', 'Boxplot is utilized for outlier analysis', 'Histogram visually displays frequency distribution', 'Covers image plotting with pillow library', 'Explains usage of quiver plot for vector plotting', 'Discusses stream plotting for vector algebra', 'Covers usage and customization of pie charts', 'Explains the process of plotting pie and donut charts using matplotlib', 'Demonstrates the use of stack plot function for creating area charts', 'Data cleaning involves removing duplicate ratings and users', 'Extracts a 2% sample set of 18,832 records', 'The most prevalent genre is fantasy', "The top-rated book is 'The Complete Calvin and Hobbes'", "The book 'The Hunger Games' is the most popular", 'The distribution of ratings shows over 6000 cases of 4-star ratings', 'The process of building a user-based collaborative filtering model', 'Extracting all of the unique user IDs and unique book IDs', 'Converting the data frame into a matrix using the as.matrix function', 'Using the recommender function to build a user-based collaborative filtering model', 'Extracting and associating the recommended book IDs with their respective titles']}, {'end': 33475.047, 'segs': [{'end': 32506.596, 'src': 'embed', 'start': 32477.534, 'weight': 7, 'content': [{'end': 32481.538, 'text': 'and in linear regression there could be more than one independent variable.', 'start': 32477.534, 'duration': 4.004}, {'end': 32486.344, 'text': "so if there's just one independent variable, it is known as simple linear regression.", 'start': 32481.538, 'duration': 4.806}, {'end': 32491.328, 'text': "and if there's more than one independent variable, It is known as multiple linear regression.", 'start': 32486.344, 'duration': 4.984}, {'end': 32500.373, 'text': 'So guys, this is the underlying concept of linear regression, where we have one dependent variable and multiple or a single independent variable.', 'start': 32491.708, 'duration': 8.665}, {'end': 32506.596, 'text': 'And we try to understand the linear relationship between the dependent variable and the independent variables.', 'start': 32500.533, 'duration': 6.063}], 'summary': 'Linear regression involves one dependent variable and multiple or single independent variables.', 'duration': 29.062, 'max_score': 32477.534, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo32477534.jpg'}, {'end': 32660.485, 'src': 'embed', 'start': 32627.103, 'weight': 0, 'content': [{'end': 32629.144, 'text': 'And this is how logistic regression works.', 'start': 32627.103, 'duration': 2.041}, {'end': 32631.453, 'text': "Now let's head on to next question.", 'start': 32630.071, 'duration': 1.382}, {'end': 32639.623, 'text': 'So what is a confusion matrix? So confusion matrix is actually a table which is used to estimate the performance of a model.', 'start': 32632.034, 'duration': 7.589}, {'end': 32645.35, 'text': 'It tabulates actual values and the predicted values in a two cross two matrix.', 'start': 32640.184, 'duration': 5.166}, {'end': 32649.174, 'text': 'So these are the actual values and these are the predicted values.', 'start': 32645.891, 'duration': 3.283}, {'end': 32651.857, 'text': 'So this what you see true positives.', 'start': 32649.655, 'duration': 2.202}, {'end': 32660.485, 'text': 'So this denotes all of those records where the actual values were true and the predicted values were also true.', 'start': 32652.277, 'duration': 8.208}], 'summary': 'Logistic regression explained; confusion matrix evaluates model performance.', 'duration': 33.382, 'max_score': 32627.103, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo32627103.jpg'}, {'end': 32870.669, 'src': 'embed', 'start': 32848.947, 'weight': 8, 'content': [{'end': 32857.983, 'text': 'So what do you understand by decision tree? So decision tree is a supervised learning algorithm which is used for both classification and regression.', 'start': 32848.947, 'duration': 9.036}, {'end': 32864.386, 'text': 'Right So decision tree can be used for both classification purpose as well as regression purpose.', 'start': 32858.604, 'duration': 5.782}, {'end': 32870.669, 'text': 'So in this case, the dependent variable can be both a numerical value as well as a categorical value.', 'start': 32864.746, 'duration': 5.923}], 'summary': 'Decision tree: supervised learning for classification and regression with both numerical and categorical variables.', 'duration': 21.722, 'max_score': 32848.947, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo32848947.jpg'}, {'end': 32981.857, 'src': 'embed', 'start': 32955.25, 'weight': 3, 'content': [{'end': 32961.057, 'text': 'So what do you understand by random forest model and also explain the working mechanism of random forest.', 'start': 32955.25, 'duration': 5.807}, {'end': 32964.261, 'text': 'So random forest is an ensemble model.', 'start': 32961.858, 'duration': 2.403}, {'end': 32969.307, 'text': 'That is, it combines multiple models together to get the final output.', 'start': 32964.622, 'duration': 4.685}, {'end': 32973.631, 'text': 'And to be precise, it combines multiple decision trees together.', 'start': 32969.847, 'duration': 3.784}, {'end': 32977.593, 'text': "Now let's understand the working mechanism of random forest model.", 'start': 32974.511, 'duration': 3.082}, {'end': 32981.857, 'text': "So let's say we have this dataset A and we have n records in it.", 'start': 32978.114, 'duration': 3.743}], 'summary': 'Random forest is an ensemble model combining multiple decision trees to derive the final output.', 'duration': 26.607, 'max_score': 32955.25, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo32955250.jpg'}, {'end': 33108.129, 'src': 'embed', 'start': 33078.18, 'weight': 1, 'content': [{'end': 33079.542, 'text': 'So I am retreading it guys.', 'start': 33078.18, 'duration': 1.362}, {'end': 33088.372, 'text': 'So each time a node is being split in a decision tree, not all 10 columns will be provided to the random forest algorithm.', 'start': 33080.083, 'duration': 8.289}, {'end': 33092.595, 'text': 'So now the question arises what would be made available to the algorithm?', 'start': 33089.033, 'duration': 3.562}, {'end': 33098.441, 'text': 'So only a random subset of these 10 columns will be available to the algorithm.', 'start': 33093.256, 'duration': 5.185}, {'end': 33102.064, 'text': "So let's say I want to split this root node now.", 'start': 33099.122, 'duration': 2.942}, {'end': 33108.129, 'text': 'So instead of providing it all the 10 columns, only a subset of the columns will be provided.', 'start': 33102.504, 'duration': 5.625}], 'summary': 'In a decision tree, only a subset of columns is provided to the random forest algorithm for splitting nodes.', 'duration': 29.949, 'max_score': 33078.18, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo33078180.jpg'}, {'end': 33323.154, 'src': 'embed', 'start': 33291.481, 'weight': 2, 'content': [{'end': 33296.381, 'text': 'So you see that there are 14,700 entries in total where the cut is equal to ideal and the price of the diamond is greater than 1000.', 'start': 33291.481, 'duration': 4.9}, {'end': 33314.932, 'text': 'So out of the 53,940 records, we have filtered out 14,700 records where the cut is equal to ideal and the price is greater than 1000.', 'start': 33296.381, 'duration': 18.551}, {'end': 33316.492, 'text': 'Now we have our second question over here.', 'start': 33314.932, 'duration': 1.56}, {'end': 33323.154, 'text': 'So on the same diamonds data set, we are supposed to make a scatter plot between the price and the carrot.', 'start': 33317.012, 'duration': 6.142}], 'summary': 'Out of 53,940 records, 14,700 have ideal cut and price >1000. next, create scatter plot for price vs. carat.', 'duration': 31.673, 'max_score': 33291.481, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo33291481.jpg'}], 'start': 32379.749, 'title': 'Concepts of regression and classification', 'summary': 'Explains linear regression in supervised learning with an example of telecom company analysis and logistic regression as a classification algorithm. it also covers confusion matrix, true positive rate, false positive rate, roc curve, decision trees, and random forest in machine learning, along with data manipulation in r.', 'chapters': [{'end': 32626.582, 'start': 32379.749, 'title': 'Linear & logistic regression concepts', 'summary': "Explains the concept of linear regression in supervised learning, with an example of a telecom company analyzing the linear relationship between monthly charges and customer tenure, and logistic regression as a classification algorithm with a detailed explanation of the s curve and its application in predicting the probability of team india winning a match based on virat kohli's runs.", 'duration': 246.833, 'highlights': ['Explains linear regression in supervised learning with an example of a telecom company analyzing the linear relationship between monthly charges and customer tenure. Example of linear relationship between monthly charges and customer tenure.', "Describes logistic regression as a classification algorithm and provides a detailed explanation of the S curve and its application in predicting the probability of Team India winning a match based on Virat Kohli's runs. Detailed explanation of S curve in logistic regression."]}, {'end': 32977.593, 'start': 32627.103, 'title': 'Understanding logistic regression, confusion matrix, and decision trees', 'summary': 'Covers the concept of logistic regression, confusion matrix, true positive rate, false positive rate, roc curve, and decision tree in machine learning, providing insights into their definitions, calculations, and applications.', 'duration': 350.49, 'highlights': ['Logistic regression, confusion matrix, true positive rate, false positive rate, ROC curve, and decision tree are explained in detail, including their definitions, calculations, and applications. This highlight encompasses the main topics covered in the transcript, summarizing the key points and the scope of the discussion.', 'Confusion matrix is used to estimate the performance of a model by tabulating actual and predicted values in a two-by-two matrix, including true positives, false negatives, false positives, and true negatives. The detailed explanation of confusion matrix, with a focus on the tabulation of actual and predicted values and the categorization into true positives, false negatives, false positives, and true negatives.', 'True positive rate, also known as sensitivity or recall, measures the percentage of actual positives correctly identified, calculated as true positives divided by all positives. A clear definition and calculation of true positive rate, emphasizing its role in measuring the accurate identification of actual positives in machine learning models.', 'The false positive rate is the probability of falsely rejecting the null hypothesis for a specific test, calculated as the ratio of false positives to the total number of actual events. An explanation of the false positive rate, including its calculation and the concept of falsely rejecting the null hypothesis in a given test scenario.', "The ROC curve, representing the trade-off between true positive rate and false positive rate, helps in evaluating model performance, with a greater area under the curve indicating a better model. A detailed overview of the ROC curve, highlighting its role in evaluating model performance and the significance of the area under the curve in determining the model's effectiveness.", 'Decision tree, a supervised learning algorithm used for both classification and regression, consists of a flowchart-like structure with root nodes, branch nodes, and leaf nodes, representing test conditions and class labels. An in-depth explanation of decision tree, emphasizing its structure, usage for classification and regression, and the representation of test conditions and class labels in the flowchart-like structure.']}, {'end': 33475.047, 'start': 32978.114, 'title': 'Random forest and data manipulation in r', 'summary': 'Explains how to use random forest to create multiple data sets from a single one, and then fit decision trees to each of these new data sets to make predictions. it also demonstrates data manipulation in r, including filtering and creating scatter plots.', 'duration': 496.933, 'highlights': ["Using random forest, it's possible to create multiple data sets from a single one by sampling with replacement, allowing for the creation of 1 million rows from just 1000 rows of data. From just one data set A, multiple data sets with the same number of records can be created, such as 1 million rows from 1000 rows of data.", 'In a random forest, each decision tree is fitted with a random subset of predictors to make them very different from each other. Random forest algorithm provides a random subset of predictors to each decision tree to make them distinct, ensuring diversity in predictions.', 'Demonstrates data manipulation in R using the dplyr package to filter records based on specific conditions and create a new data set. The dplyr package is used to filter records from the diamonds data set based on conditions such as price greater than 1000 and cut equal to ideal, resulting in 14,700 filtered records out of 53,940.', 'Illustrates the creation of a scatter plot using the ggplot2 package to visualize the relationship between price and carat, with points colored by the cut value. The ggplot2 package is employed to create a scatter plot with price on the y-axis, carat on the x-axis, and points colored by the cut value, providing insights into the relationship between these variables.']}], 'duration': 1095.298, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo32379749.jpg', 'highlights': ['Confusion matrix estimates model performance with actual and predicted values.', 'Random forest creates multiple data sets from a single one by sampling with replacement.', "Logistic regression predicts the probability of Team India winning based on Virat Kohli's runs.", 'Decision tree is a flowchart-like structure used for classification and regression.', 'ROC curve evaluates model performance by representing the trade-off between true positive rate and false positive rate.', 'True positive rate measures the percentage of actual positives correctly identified in machine learning models.', 'False positive rate is the probability of falsely rejecting the null hypothesis in a specific test.', 'Data manipulation in R using the dplyr package filters records based on specific conditions.', 'Scatter plot visualization using the ggplot2 package provides insights into the relationship between variables.']}, {'end': 37529.276, 'segs': [{'end': 34344.686, 'src': 'embed', 'start': 34316.887, 'weight': 0, 'content': [{'end': 34320.769, 'text': 'so these are all the columns and the first five records present in the data frame.', 'start': 34316.887, 'duration': 3.882}, {'end': 34325.453, 'text': 'so we have crim, zn, indus, cas, nox, age and so on.', 'start': 34320.769, 'duration': 4.684}, {'end': 34332.297, 'text': 'and for the task of simple linear regression, medv is our dependent variable and lstat is our independent variable.', 'start': 34325.453, 'duration': 6.844}, {'end': 34344.686, 'text': 'so this medv is basically the median value of price of the houses and we are trying to understand what is the median value of the price of the houses with respect to this lstat column over here.', 'start': 34332.297, 'duration': 12.389}], 'summary': 'Data frame columns and first 5 records for linear regression analysis of median house price (medv) with respect to lstat.', 'duration': 27.799, 'max_score': 34316.887, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo34316887.jpg'}, {'end': 34655.696, 'src': 'embed', 'start': 34628.548, 'weight': 4, 'content': [{'end': 34635.731, 'text': 'So as LSTAT increases, MEDV decreases and that is the reason this coefficient is associated with a negative value.', 'start': 34628.548, 'duration': 7.183}, {'end': 34640.453, 'text': "So now that we've built the model, it's time to predict the values on top of the test set.", 'start': 34636.211, 'duration': 4.242}, {'end': 34646.021, 'text': "So again, I'll use this instance and I will use the predict function with the help of this instance.", 'start': 34640.953, 'duration': 5.068}, {'end': 34655.696, 'text': "So regressor dot predict, and I will pass in this X test object inside this function, and I'll store this in the Y pred object.", 'start': 34646.281, 'duration': 9.415}], 'summary': 'As lstat increases, medv decreases. model used to predict values on test set.', 'duration': 27.148, 'max_score': 34628.548, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo34628548.jpg'}, {'end': 34725.01, 'src': 'embed', 'start': 34695.47, 'weight': 2, 'content': [{'end': 34700.333, 'text': 'So again the lower the values of these three it means the better the model is.', 'start': 34695.47, 'duration': 4.863}, {'end': 34707.898, 'text': "So let's say if we are comparing these values with some other model and if these values are less than that second model,", 'start': 34700.794, 'duration': 7.104}, {'end': 34710.38, 'text': 'then the first model would be better than the second model.', 'start': 34707.898, 'duration': 2.482}, {'end': 34714.042, 'text': 'So that was implementing simple linear regression in Python.', 'start': 34710.88, 'duration': 3.162}, {'end': 34719.046, 'text': 'So now we are supposed to implement logistic regression on this hard data set,', 'start': 34714.503, 'duration': 4.543}, {'end': 34725.01, 'text': 'where the dependent variable is this target column and the independent variable is the age column.', 'start': 34719.046, 'duration': 5.964}], 'summary': 'Implementing simple linear regression and logistic regression in python.', 'duration': 29.54, 'max_score': 34695.47, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo34695470.jpg'}, {'end': 34959.662, 'src': 'embed', 'start': 34928.833, 'weight': 1, 'content': [{'end': 34931.513, 'text': 'So we have something known as null deviance and residual deviance.', 'start': 34928.833, 'duration': 2.68}, {'end': 34935.794, 'text': 'So simply put the lower the deviance value the better the model.', 'start': 34932.033, 'duration': 3.761}, {'end': 34941.356, 'text': "So this null deviance basically tells us the deviance of the model when we don't have any independent variables.", 'start': 34936.095, 'duration': 5.261}, {'end': 34947.838, 'text': 'That is when we are trying to predict the value of the target column with only the intercept.', 'start': 34941.656, 'duration': 6.182}, {'end': 34952.299, 'text': "So if that's the case then null deviance is 417.64.", 'start': 34948.078, 'duration': 4.221}, {'end': 34959.662, 'text': 'And residual deviance is that deviance when we include the independent variables and try to predict the target column.', 'start': 34952.299, 'duration': 7.363}], 'summary': 'Lower deviance values indicate better model fit; null deviance is 417.64.', 'duration': 30.829, 'max_score': 34928.833, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo34928833.jpg'}, {'end': 35234.618, 'src': 'embed', 'start': 35206.597, 'weight': 6, 'content': [{'end': 35210.761, 'text': 'So this time the formula is the same, which is basically target, tilde symbol, age.', 'start': 35206.597, 'duration': 4.164}, {'end': 35213.303, 'text': 'So target is the dependent variable.', 'start': 35211.201, 'duration': 2.102}, {'end': 35220.569, 'text': "age is the independent variable and we're trying to determine the probability of the patient having heart disease with respect to his age.", 'start': 35213.303, 'duration': 7.266}, {'end': 35228.462, 'text': 'and we are building this model on top of the train set and since this is a logistic regression model, the family would be equal to binomial,', 'start': 35221.189, 'duration': 7.273}, {'end': 35231.877, 'text': "and i'll store this result in log mod 2..", 'start': 35228.462, 'duration': 3.415}, {'end': 35234.618, 'text': "Now that we've built the model, it's time to predict the values.", 'start': 35231.877, 'duration': 2.741}], 'summary': 'Logistic regression model to predict heart disease probability based on age.', 'duration': 28.021, 'max_score': 35206.597, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo35206597.jpg'}, {'end': 36237.478, 'src': 'embed', 'start': 36180.842, 'weight': 5, 'content': [{'end': 36188.187, 'text': 'this states that there is one record where the actual value was virginica, but it has been incorrectly classified as versicolor.', 'start': 36180.842, 'duration': 7.345}, {'end': 36190.109, 'text': 'Similarly, this 16 which we see.', 'start': 36188.608, 'duration': 1.501}, {'end': 36198.195, 'text': 'So, out of the 16 records which were actually virginica, all of those 16 records have been correctly classified as virginica.', 'start': 36190.609, 'duration': 7.586}, {'end': 36204.08, 'text': 'Now again, to find out the accuracy, we are supposed to divide this left diagonal with all of the values.', 'start': 36198.616, 'duration': 5.464}, {'end': 36217.967, 'text': "So that'll be 17 plus 16 plus 16 divided by 17 plus 16 plus 16 plus 1 and 1.", 'start': 36205.882, 'duration': 12.085}, {'end': 36222.57, 'text': "So you see that we get an accuracy of 96% for the model which we've built.", 'start': 36217.967, 'duration': 4.603}, {'end': 36228.653, 'text': "So now we'd have to go and build a random forest model on top of the CTG dataset,", 'start': 36223.13, 'duration': 5.523}, {'end': 36233.496, 'text': 'where NSP is the dependent variable and all other columns are independent variables.', 'start': 36228.653, 'duration': 4.843}, {'end': 36237.478, 'text': "So let's head on to RStudio and implement the random forest model.", 'start': 36234.116, 'duration': 3.362}], 'summary': 'The model achieved 96% accuracy in classifying virginica, next step is to build a random forest model on the ctg dataset in rstudio.', 'duration': 56.636, 'max_score': 36180.842, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo36180842.jpg'}], 'start': 33475.466, 'title': 'Data analysis and model building', 'summary': 'Covers various topics including imputing missing values, simple linear regression, data partitioning, model building, logistic regression, and decision tree models. it also discusses job trends and programming concepts, with python being the most preferred language.', 'chapters': [{'end': 33776.936, 'start': 33475.466, 'title': 'Imputing missing values and simple linear regression', 'summary': 'Demonstrates introducing 25% missing values in the iris dataset, imputing sepal length with the mean and petal length with the median, and implementing simple linear regression on the empty cars dataset in r.', 'duration': 301.47, 'highlights': ['Introducing 25% missing values in the iris dataset using the Miss Forest package and imputing sepal length with the mean and petal length with the median. The speaker introduces 25% missing values in the iris dataset using the Miss Forest package, and imputes the sepal length with the mean and the petal length with the median.', 'Implementing simple linear regression in R on the empty cars dataset with MPG as the dependent variable and displacement as the independent variable. The chapter demonstrates implementing simple linear regression on the empty cars dataset with MPG as the dependent variable and displacement as the independent variable.']}, {'end': 34257.206, 'start': 33776.936, 'title': 'Data partitioning and model building', 'summary': 'Discusses the importance of dividing a dataset into training and testing sets to avoid overfitting, demonstrates the process of data partitioning using the createdatapartition function from the carrot package, builds a linear model on the training set, predicts values for the test set, and calculates the root mean square error (rmse) value for the model, which is found to be 4.33, highlighting the significance of lower rmse values for better model performance.', 'duration': 480.27, 'highlights': ['The RMSE value for the model built is 4.33, emphasizing the importance of lower RMSE values for better model performance. RMSE value: 4.33', 'The model is built on the training set using the simple linear model function (lm) with mpg as the dependent variable and displacement as the independent variable. Model building using lm function: dependent variable - mpg, independent variable - displacement', 'The data set is divided into training and testing sets using the createDataPartition function with a split ratio of 65% for training and 35% for testing. Data partitioning: split ratio - 65% training, 35% testing']}, {'end': 35103.362, 'start': 34257.967, 'title': 'Implementing linear and logistic regression in python', 'summary': 'Covers implementing simple linear regression in python on the boston dataset, showing an inverse relationship between medv and lstat, achieving a mean absolute error of 4.69 and mean squared error of 43. it also delves into implementing logistic regression on the heart dataset, revealing a strong relationship between age and the target column, and predicting the probability of a person having heart disease based on their age.', 'duration': 845.395, 'highlights': ['Showing inverse relationship between MEDV and LSTAT Demonstrates an inverse relationship between the median value of house prices (MEDV) and the percentage of lower status population (LSTAT), indicating that as LSTAT increases, MEDV decreases.', 'Achieving a mean absolute error of 4.69 and mean squared error of 43 Demonstrates the accuracy of the simple linear regression model, with lower values indicating a better model performance.', 'Revealing strong relationship between age and the target column Highlights the strong relationship between age and the target column in the logistic regression model, as indicated by the rejection of the null hypothesis and the reduction in deviance when including the age column.', 'Predicting the probability of a person having heart disease based on their age Illustrates the prediction of the probability of a person having heart disease at different age values, showing the likelihood of heart disease increasing as age increases.']}, {'end': 35590.965, 'start': 35103.642, 'title': 'Building logistic regression model in r', 'summary': "Discusses the process of dividing the dataset into train and test sets, building a logistic regression model on the train set, predicting values on the test set, evaluating the model's accuracy, and creating an roc curve, achieving an overall accuracy of 53% for the model.", 'duration': 487.323, 'highlights': ['The dataset is divided into train and test sets with a 70-30 split ratio, resulting in 213 records in the training set and 90 records in the testing set. The split ratio is set at 70-30, yielding 213 records in the training set and 90 records in the testing set.', "The logistic regression model is built on the training set with the formula 'target ~ age', using a binomial family, and results in a model stored in log mod 2. The logistic regression model is built on the training set with the formula 'target ~ age', using a binomial family, and the resulting model is stored in log mod 2.", 'The accuracy of the model is calculated to be 53% using a confusion matrix and a probability threshold of 0.6 for classification. The accuracy of the model is determined to be 53% using a confusion matrix and a probability threshold of 0.6 for classification.']}, {'end': 36098.26, 'start': 35591.586, 'title': 'Building logistic regression and decision tree models', 'summary': 'Covers building a logistic regression model to predict customer churn based on monthly charges, resulting in a log loss of 0.55, and then constructing a decision tree model to predict iris species with an accuracy of 65% in the test set.', 'duration': 506.674, 'highlights': ["Building Logistic Regression Model A logistic regression model is constructed to predict customer churn based on monthly charges, resulting in a log loss of 0.55, indicating the model's performance.", 'Dividing Data into Training and Testing Sets The data is divided into training and testing sets using a 70-30 split, ensuring 70% records for training and 30% for testing.', "Constructing Decision Tree Model for Iris Species Prediction A decision tree model is built to predict iris species, achieving an accuracy of 65% in the test set, and the model's structure is visually presented."]}, {'end': 36624.837, 'start': 36098.62, 'title': 'Predictive modeling in r', 'summary': 'Discusses building and predicting values using the decision tree model and evaluating a 96% accurate random forest model in r, along with the process of data preprocessing and model evaluation.', 'duration': 526.217, 'highlights': ['Confusion Matrix for Decision Tree Model The confusion matrix for the decision tree model shows that out of 50 records, 49 have been correctly classified, resulting in an accuracy of 98%.', 'Building Random Forest Model The process of building a random forest model on the CTG dataset with a seed value of 222 and the model achieving a high accuracy of 96%.', 'Data Preprocessing The transcript covers the process of converting an integer column to a factor and dividing the dataset into training and testing sets with 1383 and 743 records, respectively.']}, {'end': 37529.276, 'start': 36628.371, 'title': 'Python job trends and programming concepts', 'summary': 'Discusses the job trends of different programming languages, with python being the most preferred language, and covers interview questions on python keywords, literals, dictionaries, classes and objects, init method, and inheritance, with detailed examples and explanations.', 'duration': 900.905, 'highlights': ['Python is the most preferred language across various industries, steadily increasing in popularity over the years. Python is the most preferred language across various industries, steadily increasing in popularity over the years.', 'There are 33 keywords in Python 3.7, and they are case sensitive. There are 33 keywords in Python 3.7, and they are case sensitive.', 'Python has four types of literals: string, numeric, boolean, and special literals. Python has four types of literals: string, numeric, boolean, and special literals.', 'A dictionary in Python is an unordered collection of elements stored as key-value pairs. A dictionary in Python is an unordered collection of elements stored as key-value pairs.', 'Classes in Python are blueprints, and objects are real world entities created from classes. Classes in Python are blueprints, and objects are real world entities created from classes.', 'The init method in Python is used to initialize variables and acts as a constructor. The init method in Python is used to initialize variables and acts as a constructor.', "Inheritance in Python refers to one class acquiring the properties of another class, demonstrated with an example of inheriting a base class 'fruit' in a 'citrus' class. Inheritance in Python refers to one class acquiring the properties of another class, demonstrated with an example of inheriting a base class 'fruit' in a 'citrus' class."]}], 'duration': 4053.81, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo33475466.jpg', 'highlights': ['Python is the most preferred language across various industries, steadily increasing in popularity over the years.', 'Building Random Forest Model achieving a high accuracy of 96% on the CTG dataset.', 'Confusion Matrix for Decision Tree Model shows an accuracy of 98% with 49 out of 50 records correctly classified.', 'Constructing Decision Tree Model for Iris Species Prediction achieving an accuracy of 65% in the test set.', 'Predicting the probability of a person having heart disease based on their age.', 'Showing inverse relationship between MEDV and LSTAT.', 'Introducing 25% missing values in the iris dataset using the Miss Forest package and imputing sepal length with the mean and petal length with the median.', 'Implementing simple linear regression in R on the empty cars dataset with MPG as the dependent variable and displacement as the independent variable.']}, {'end': 38516.091, 'segs': [{'end': 37985.14, 'src': 'embed', 'start': 37962.166, 'weight': 2, 'content': [{'end': 37969.731, 'text': 'now to convert this list into a data frame, all I have to do is use PD dot data frame function so over here.', 'start': 37962.166, 'duration': 7.565}, {'end': 37981.258, 'text': "we need to keep in mind that D is capital right PD dot data frame and I will pass in L1 inside this and I will store this in, let's say,", 'start': 37969.731, 'duration': 11.527}, {'end': 37985.14, 'text': "data 1 and I'll just print out data 1 over here.", 'start': 37981.258, 'duration': 3.882}], 'summary': 'Convert list to dataframe using pd.dataframe function with l1, storing in data1.', 'duration': 22.974, 'max_score': 37962.166, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo37962166.jpg'}, {'end': 38090.948, 'src': 'embed', 'start': 38063.889, 'weight': 1, 'content': [{'end': 38068.592, 'text': "Now out of this we'd have to extract some specified rows based on a condition.", 'start': 38063.889, 'duration': 4.703}, {'end': 38078.319, 'text': "So we'd have to extract only those records where the sepal length value is greater than 5 and the sepal width value is greater than 3.", 'start': 38068.913, 'duration': 9.406}, {'end': 38083.603, 'text': "So I'll start off by loading the pandas library, import pandas as pd.", 'start': 38078.319, 'duration': 5.284}, {'end': 38090.948, 'text': "And after this, to load a CSV file, I'd have to use the pd.readcsv function.", 'start': 38084.223, 'duration': 6.725}], 'summary': 'Extract rows with sepal length > 5 and sepal width > 3 using pandas.', 'duration': 27.059, 'max_score': 38063.889, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo38063889.jpg'}, {'end': 38143.453, 'src': 'embed', 'start': 38113.661, 'weight': 0, 'content': [{'end': 38120.685, 'text': "Now let's see how can we extract only those records where sepal length is greater than five and sepal width is greater than three, right?", 'start': 38113.661, 'duration': 7.024}, {'end': 38128.856, 'text': "So I'll start off by giving the name of the data frame and I'll put in these braces over here and I'll give in the first condition.", 'start': 38121.192, 'duration': 7.664}, {'end': 38131.378, 'text': 'So the first condition is.', 'start': 38129.577, 'duration': 1.801}, {'end': 38143.453, 'text': "again iris, and from this I'd have to select only those sepal length columns where it is greater than 5.", 'start': 38133.266, 'duration': 10.187}], 'summary': 'Extract records with sepal length > 5 and sepal width > 3 from iris dataset.', 'duration': 29.792, 'max_score': 38113.661, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo38113661.jpg'}, {'end': 38217.432, 'src': 'embed', 'start': 38188.969, 'weight': 4, 'content': [{'end': 38193.833, 'text': "so what we do is we're given the first condition where sepal length is greater than 5.", 'start': 38188.969, 'duration': 4.864}, {'end': 38199.418, 'text': "after that we'll use the AND operator and then we give the second condition, which is sepal width is greater than 3.", 'start': 38193.833, 'duration': 5.585}, {'end': 38212.788, 'text': "right. so after this we again have this iris data frame over here and we'd have to introduce nan values or null values in the first 10 rows of sepal width column and petal width columns.", 'start': 38199.418, 'duration': 13.37}, {'end': 38217.432, 'text': 'so if you see this original data frame over here, you see that these two columns comprise of some values,', 'start': 38212.788, 'duration': 4.644}], 'summary': 'Using conditions, introduce null values in sepal and petal width columns of first 10 rows.', 'duration': 28.463, 'max_score': 38188.969, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo38188969.jpg'}], 'start': 37529.276, 'title': 'Python inheritance, numpy arrays, and data manipulation basics', 'summary': 'Covers single level inheritance in python, how to create 1d and 2d numpy arrays, and finding the n largest values from a numpy array. it also includes data manipulation basics such as creating data frames, extracting specified rows, handling nan values, and file operations in python.', 'chapters': [{'end': 37769.788, 'start': 37529.276, 'title': 'Python inheritance and numpy arrays', 'summary': 'Covers single level inheritance in python and explains how to create 1d and 2d numpy arrays, with examples and operations, such as addition, using numpy library.', 'duration': 240.512, 'highlights': ['Explaining NumPy and its usage for linear algebra and mathematical operations NumPy is widely used for linear algebra and mathematical operations on arrays.', 'Creating 1D and 2D NumPy arrays and initializing a 5x5 NumPy array with zeros Demonstrates the creation of 1D and 2D NumPy arrays, as well as initializing a 5x5 array with zeros using NumPy library.', 'Performing addition of individual elements in NumPy arrays and explaining the axis parameter Illustrates the addition of individual elements in NumPy arrays and explains the use of the axis parameter in NumPy sum method.']}, {'end': 37913.579, 'start': 37770.248, 'title': 'Numpy array: finding n largest values', 'summary': 'Demonstrates how to find the n largest values from a numpy array using np.argsort, obtaining the indices of values arranged in ascending order and then arranging them in descending order to get the first two highest values, which are 100 and 68 from a numpy array comprising 103,4567 elements.', 'duration': 143.331, 'highlights': ['The chapter demonstrates how to find the n largest values from a numpy array using np.argsort. The np.argsort function is used to obtain the indices of values arranged in ascending order.', 'The first two highest values, which are 100 and 68, are obtained from a numpy array comprising 103,4567 elements. The process involves arranging the indices in descending order to obtain the first two highest values.']}, {'end': 38516.091, 'start': 37913.92, 'title': 'Python data manipulation basics', 'summary': 'Covers creating data frames from lists and dictionaries, extracting specified rows based on conditions, introducing nan values, finding the count of nan values in columns, and opening and reading a file in python.', 'duration': 602.171, 'highlights': ['Creating data frames from lists and dictionaries The speaker explains how to create a data frame from a list and a dictionary, which is a common question in Python interviews, and provides examples with quantifiable data.', 'Extracting specified rows based on conditions The speaker demonstrates how to extract specific rows from a dataset based on conditions, with a specific example of extracting records where sepal length is greater than 5 and sepal width is greater than 3.', 'Introducing NaN values The speaker shows how to introduce NaN values in specific columns of a data frame, providing a clear example of introducing NaN values in the first 10 rows of two columns and verifying the changes.', 'Finding the count of NaN values in columns The process of finding the count of NaN values in each column of a data frame is explained, including the use of specific functions and the display of the count of NaN values in each column with quantifiable data.', "Opening and reading a file in Python The speaker demonstrates the process of opening and reading a file in Python, providing a specific example of opening a file named 'Sparta' and reading the content from it."]}], 'duration': 986.815, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo37529276.jpg', 'highlights': ['Creating 1D and 2D NumPy arrays and initializing a 5x5 NumPy array with zeros Demonstrates the creation of 1D and 2D NumPy arrays, as well as initializing a 5x5 array with zeros using NumPy library.', 'Creating data frames from lists and dictionaries The speaker explains how to create a data frame from a list and a dictionary, which is a common question in Python interviews, and provides examples with quantifiable data.', 'Finding the count of NaN values in columns The process of finding the count of NaN values in each column of a data frame is explained, including the use of specific functions and the display of the count of NaN values in each column with quantifiable data.', 'Extracting specified rows based on conditions The speaker demonstrates how to extract specific rows from a dataset based on conditions, with a specific example of extracting records where sepal length is greater than 5 and sepal width is greater than 3.', 'The chapter demonstrates how to find the n largest values from a numpy array using np.argsort. The np.argsort function is used to obtain the indices of values arranged in ascending order.']}, {'end': 40073.731, 'segs': [{'end': 38594.535, 'src': 'embed', 'start': 38565.551, 'weight': 1, 'content': [{'end': 38568.675, 'text': 'so this is how we can create a simple lambda function.', 'start': 38565.551, 'duration': 3.124}, {'end': 38572.261, 'text': "now I'll call the function and pass in a number.", 'start': 38568.675, 'duration': 3.586}, {'end': 38574.923, 'text': "So let's say I'll pass in 8.", 'start': 38572.462, 'duration': 2.461}, {'end': 38576.924, 'text': 'Now this is returning 18.', 'start': 38574.923, 'duration': 2.001}, {'end': 38580.186, 'text': "So all I'm doing is adding 10 to the number which I'm passing into this.", 'start': 38576.924, 'duration': 3.262}, {'end': 38584.549, 'text': "Now again, let's say if I pass 5 into this, I'll get 15.", 'start': 38580.707, 'duration': 3.842}, {'end': 38588.171, 'text': "Similarly, let's say if I pass 100, I'll get 110.", 'start': 38584.549, 'duration': 3.622}, {'end': 38594.535, 'text': 'So we have successfully created a lambda function which takes in a parameter and adds 10 to the given parameter.', 'start': 38588.171, 'duration': 6.364}], 'summary': 'Demonstrating a lambda function adding 10 to a given parameter.', 'duration': 28.984, 'max_score': 38565.551, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo38565551.jpg'}, {'end': 38644.057, 'src': 'embed', 'start': 38614.137, 'weight': 7, 'content': [{'end': 38615.599, 'text': "So let's quickly head on to Jupyter Notebook.", 'start': 38614.137, 'duration': 1.462}, {'end': 38618.24, 'text': "I'll start off by loading the required packages.", 'start': 38616.019, 'duration': 2.221}, {'end': 38620.621, 'text': 'so I would need the numpy package.', 'start': 38618.24, 'duration': 2.381}, {'end': 38626.482, 'text': "so I'll type in import numpy as np and I would also need the matplotlib package.", 'start': 38620.621, 'duration': 5.861}, {'end': 38639.852, 'text': "so I'll type in from matplotlib import pyplot as plt right.", 'start': 38626.482, 'duration': 13.37}, {'end': 38644.057, 'text': "so now that we've loaded the required packages, let's go ahead and create our data.", 'start': 38639.852, 'duration': 4.205}], 'summary': 'Using jupyter notebook, numpy, and matplotlib to load packages and create data.', 'duration': 29.92, 'max_score': 38614.137, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo38614137.jpg'}, {'end': 39066.445, 'src': 'embed', 'start': 39045.698, 'weight': 0, 'content': [{'end': 39055.841, 'text': "so what is happening over here is initially i's value is zero and it will loop through the all of the characters of the string which is present in a.", 'start': 39045.698, 'duration': 10.143}, {'end': 39058.042, 'text': "and so let's say the loop starts over here.", 'start': 39055.841, 'duration': 2.201}, {'end': 39061.283, 'text': "initially i's value is zero and it will enter o.", 'start': 39058.042, 'duration': 3.241}, {'end': 39063.324, 'text': 'now the count increments by one.', 'start': 39061.283, 'duration': 2.041}, {'end': 39064.704, 'text': 'again it will head to the next character.', 'start': 39063.324, 'duration': 1.38}, {'end': 39066.445, 'text': 'the count again would increment by one.', 'start': 39064.704, 'duration': 1.741}], 'summary': 'Loop through string, increment count for each character.', 'duration': 20.747, 'max_score': 39045.698, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo39045698.jpg'}, {'end': 39751.732, 'src': 'embed', 'start': 39725.803, 'weight': 2, 'content': [{'end': 39731.327, 'text': 'Y train is the train set for the target and Y test is the test set for the target.', 'start': 39725.803, 'duration': 5.524}, {'end': 39735.129, 'text': 'So we have our training and testing sets ready.', 'start': 39732.227, 'duration': 2.902}, {'end': 39737.471, 'text': 'Let me click on run over here.', 'start': 39735.85, 'duration': 1.621}, {'end': 39742.861, 'text': "Alright, now it's finally time to build the model on top of the train set.", 'start': 39738.936, 'duration': 3.925}, {'end': 39749.409, 'text': "So from sklearn.linear model, I will import the linear regression and I'll create an instance of this.", 'start': 39743.281, 'duration': 6.128}, {'end': 39751.732, 'text': "So I'll name that instance to be regressor.", 'start': 39749.829, 'duration': 1.903}], 'summary': 'Using y train and y test, build a model with linear regression from sklearn.', 'duration': 25.929, 'max_score': 39725.803, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo39725803.jpg'}, {'end': 39953.913, 'src': 'embed', 'start': 39928.951, 'weight': 3, 'content': [{'end': 39934.656, 'text': 'So 30% of the records would go into the test set and the rest, 70% of the records, would go into the training set.', 'start': 39928.951, 'duration': 5.705}, {'end': 39938.86, 'text': 'and I am storing the results into xtrain, xtest, ytrain and ytest.', 'start': 39934.656, 'duration': 4.204}, {'end': 39946.947, 'text': 'So xtrain is the training set for the features, xtest is a test set for the features, ytrain is the training set for the target and.', 'start': 39939.4, 'duration': 7.547}, {'end': 39949.549, 'text': 'ytest is the test set for the target.', 'start': 39946.947, 'duration': 2.602}, {'end': 39953.913, 'text': "Now we'll import decision tree classifier from sklearn.tree.", 'start': 39950.11, 'duration': 3.803}], 'summary': 'Splitting 30% records for testing, 70% for training, using decision tree classifier', 'duration': 24.962, 'max_score': 39928.951, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo39928951.jpg'}], 'start': 38516.752, 'title': 'Python data manipulation and visualization', 'summary': 'Covers lambda functions, creating line and bar plots using matplotlib, modules in python, list shuffling, string length, array manipulation, common items in numpy arrays, pandas series and dataframe operations, and building a decision tree classifier on iris data with 91.11% accuracy.', 'chapters': [{'end': 38993.35, 'start': 38516.752, 'title': 'Lambda functions, line plot, bar plot, modules & list shuffling', 'summary': 'Discusses lambda functions, creating a simple line plot and bar plot using matplotlib, the concept of modules in python for organizing code, and randomizing items of a list using the shuffle function from the random library.', 'duration': 476.598, 'highlights': ['The chapter discusses lambda functions, which are anonymous functions in Python, and demonstrates creating a lambda function to add 10 to a given number, producing output of 18, 15, and 110 for input values of 8, 5, and 100 respectively. Demonstrates creating a lambda function to add 10 to a given number, producing output of 18, 15, and 110 for input values of 8, 5, and 100 respectively.', 'It explains the process of creating a simple line plot using the numpy and matplotlib packages, showcasing the generation of x-axis and y-axis values ranging from 0 to 10 and labeling the plot axes and title. Explains the process of creating a simple line plot using the numpy and matplotlib packages, showcasing the generation of x-axis and y-axis values ranging from 0 to 10 and labeling the plot axes and title.', 'The chapter demonstrates the creation of a simple bar plot using the matplotlib package, showcasing the representation of fruits and their costs on the x-axis and y-axis respectively. Demonstrates the creation of a simple bar plot using the matplotlib package, showcasing the representation of fruits and their costs on the x-axis and y-axis respectively.', 'It introduces the concept of modules in Python to organize code, using the example of creating separate modules for addition, subtraction, multiplication, and division in a calculator program. Introduces the concept of modules in Python to organize code, using the example of creating separate modules for addition, subtraction, multiplication, and division in a calculator program.', 'The chapter explains the process of randomizing the items of a list in place in Python by utilizing the shuffle function from the random library, demonstrating the shuffling of a given list of elements. Explains the process of randomizing the items of a list in place in Python by utilizing the shuffle function from the random library, demonstrating the shuffling of a given list of elements.']}, {'end': 39253.527, 'start': 38993.791, 'title': 'String length, array manipulation, and common items in numpy arrays', 'summary': 'Covers how to find the length of a string without using the len function using a for loop, replacing odd numbers in a numpy array with -1, and finding the common items between two numpy arrays.', 'duration': 259.736, 'highlights': ['Finding the length of a string using a for loop The program uses a for loop to iterate through the characters of the string to count and determine the number of characters present, resulting in a string length of 13.', 'Replacing odd numbers in a NumPy array with -1 The odd numbers in the given NumPy array (0-9) are replaced with -1 by checking the remainder when each element is divided by 2, resulting in the odd numbers (1, 3, 5, 7, 9) being replaced with -1.', 'Finding common items between two NumPy arrays The np.intersect1d method is used to identify and extract the common items (2, 4) present in two given NumPy arrays.']}, {'end': 39819.925, 'start': 39253.527, 'title': 'Pandas series and dataframe operations', 'summary': 'Demonstrates the conversion of elements into title case using pandas series, calculation of the number of characters in each word of the series, renaming columns in a dataframe, and implementing a linear regression model on the boston dataset with an 80-20 train-test split.', 'duration': 566.398, 'highlights': ['Implementing a linear regression model on the Boston dataset with an 80-20 train-test split The chapter covers the implementation of a linear regression model on the Boston dataset, with RM as the independent variable and MEDV as the dependent variable, along with an 80-20 train-test split.', 'Conversion of elements into title case using Pandas series The demonstration includes the conversion of elements into title case using the Pandas series and the map method, which successfully converts all elements into title case.', 'Calculation of the number of characters in each word of the series The process involves using the map method and a lambda function to calculate the number of characters in each word of the series, providing insights into the character count for each word.', 'Renaming columns in a dataframe The chapter explains the renaming of columns in a dataframe using the Pandas rename method, which allows for easy and efficient renaming of specific columns.']}, {'end': 40073.731, 'start': 39820.765, 'title': 'Decision tree classifier on iris data', 'summary': 'Demonstrates building a decision tree classifier on iris data with 70-30 train-test split, achieving 91.11% accuracy and evaluating the model using confusion matrix.', 'duration': 252.966, 'highlights': ['The accuracy of the decision tree classifier model is 91.11%, determined using the confusion matrix, where 12 setosa, 16 versicolor, and 13 virginica species were classified correctly out of the total records.', 'The chapter performs a 70-30 train-test split on the iris data, with 30% of the records allocated to the test set and the rest 70% to the training set, using sklearn.model_selection.', 'The transcript details the process of building a decision tree classifier on iris data, separating features and the target variable, and fitting the model to achieve the final interview question.']}], 'duration': 1556.979, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QtYOI-9R1vo/pics/QtYOI-9R1vo38516752.jpg', 'highlights': ['Building a decision tree classifier on iris data with 91.11% accuracy', 'Creating a simple bar plot using the matplotlib package', 'Creating a simple line plot using the numpy and matplotlib packages', 'Demonstrating a lambda function to add 10 to a given number', 'Finding common items between two NumPy arrays', 'Renaming columns in a dataframe', 'Conversion of elements into title case using Pandas series', 'Randomizing the items of a list in place in Python']}], 'highlights': ['Demo achieves 88.14% accuracy in predicting heart disease using k-means clustering and logistic regression', 'Course covers data science fundamentals, essential skills, probability concepts, machine learning, data analysis, visualization techniques, regression, and classification algorithms, with practical demonstrations achieving 88.14% accuracy in predicting heart disease and 91.11% accuracy in decision tree classification', 'The average salary for a senior data scientist in the US is about $185,000', 'The average salary for a junior data scientist in the US is around $140,000', 'The average salary for a senior data scientist in India is about 21.5 lakhs per annum', 'The average salary for a junior data scientist in India is approximately 15.7 lakhs per annum', 'Understanding mathematics, statistics, probability, calculus, and linear algebra is crucial for becoming a data scientist, even for complete beginners', 'Building a portfolio with small projects and solving real-world problems using data science skills is crucial for gaining a competitive edge and increasing chances of getting shortlisted for data science jobs', 'The most popular languages for data science tasks are Python and R, with good support for machine learning', 'The demand for data scientists has increased due to the abundance of information and the lack of resources to analyze it, leading to a huge demand for data scientists', 'Data science is the process of finding hidden patterns from the raw and structured data, with unstructured data accounting for 80% of data gathered by companies']}