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Data Science Course | Data Science Training | Data Science Tutorial for Beginners | Intellipaat

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ðŸ”µ Intellipaat Data Science course: https://intellipaat.com/data-science-architect-masters-program-training/
In this video on Data Science course, you will learn Data Science from basic to advance level along with projects and interview questions. This complete Data Science training helps you to learn the Data Science principles, tools, algorithms, and methodologies that make it a powerful medium to solve business problems. This is a must watch session for everyone who wishes to learn data science and make a career in it.
#DataScienceCourse #DataScienceTraining #DataScienceTutorialForBeginners #DataScience
00:00 - Introduction
06:47 - Data Science Impact
08:47 - Case Study - Finance
11:05 - What is Data Science?
11:36 - Data Science and Statistics
13:38 - Introduction to ML
14:34 - How ML Works?
20:51 - Types of ML
39:56 - What is Regression?
44:47 - Types of Regression
50:14 - what is Linear Regression?
01:17:24 - Introduction to Logistic Regression
01:27:12 - Regression Algorithm
01:45:35 - What is Classification?
01:54:20 - What is Scikit Learn?
02:13:12 - What is Numpy?
02:20:28 - Initializing Numpy Array?
02:55:18 - Numpy Broadcasting
03:08:18 - Numpy Indexing and Slicing
03:18:06 - Characteristics of Scipy
03:46:03 - Introduction to Pandas
03:54:14 - Pandas: Series Object
04:05:45 - How to perform a merge operation?
04:17:51 - Importing & Analysing the Dataset
04:22:37 - Cleaning the Dataset
04:29:50 - Types of Data Science Roles
05:36:48 - Tricks of the Trade
05:47:04 - Types of Questions
06:02:54 - Questions for the Interviewer
06:17:14 - Recommendation Enigine
06:51:56 - User-Based collaborative filtering
07:15:08 - Item-Item collaborative filtering
08:13:48 - Case Study
08:23:14 - Tasks to be performed
09:24:10 - Skills Required to Become a Data Scientist
09:38:30 - Trends in Data Science Science
09:45:22 - Prerequisites to become a data scientist
09:56:03 - Data Science vs Data Analytics
10:00:00 - In a Gist!
10:03:06 - Data Science Interview Questions
ðŸ”µ Read complete Data Science tutorial here: https://intellipaat.com/blog/tutorial/data-science-tutorial/
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ðŸ”µ Read insightful blog on what is Data Science: https://intellipaat.com/blog/what-is-data-science/
ðŸ”µ Interested to know about Data Science certifications? Read this blog: https://intellipaat.com/blog/data-science-certification/
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{'title': 'Data Science Course | Data Science Training | Data Science Tutorial for Beginners | Intellipaat', 'heatmap': [{'end': 29433.231, 'start': 29023.952, 'weight': 1}], 'summary': 'Covers various data science topics including insights, ai applications, regression models, machine learning, scikit-learn, numpy, scipy, pandas, interviews, churn prediction, recommendation engines, collaborative filtering, association rule analysis, essential skills, certifications, careers, regression analysis, and implementing logistic and decision tree models, achieving a mean absolute error of 4.69, mean squared error of 43, root mean squared error of 6.62, maximum probability of 82%, and 96% accuracy.', 'chapters': [{'end': 465.873, 'segs': [{'end': 77.55, 'src': 'embed', 'start': 37.523, 'weight': 0, 'content': [{'end': 39.384, 'text': 'how is it going to help them to become one??', 'start': 37.523, 'duration': 1.861}, {'end': 50.177, 'text': 'So, guys, the way we designed this course was we basically had a look at all the resumes and the job postings which are related to data scientists.', 'start': 40.768, 'duration': 9.409}, {'end': 57.444, 'text': 'We noted down all the skill sets that are required and then we got them confirmed from hiring managers,', 'start': 50.818, 'duration': 6.626}, {'end': 60.227, 'text': 'who basically hired data scientists day in and day out.', 'start': 57.444, 'duration': 2.783}, {'end': 67.884, 'text': 'Later. what we did was we came up with the curriculum and we got them confirmed by data scientists who teach with us,', 'start': 60.959, 'duration': 6.925}, {'end': 74.288, 'text': 'and when they basically affirmed us that this curriculum looks good, we made it available to our learners right?', 'start': 67.884, 'duration': 6.404}, {'end': 77.55, 'text': 'So somebody who is enrolling with us for the data science course,', 'start': 74.508, 'duration': 3.042}], 'summary': 'Curriculum was designed based on job postings and confirmed by hiring managers and data scientists.', 'duration': 40.027, 'max_score': 37.523, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk37523.jpg'}, {'end': 338.254, 'src': 'embed', 'start': 310.456, 'weight': 4, 'content': [{'end': 314.559, 'text': 'So they want to understand what future do we have as a data scientist?', 'start': 310.456, 'duration': 4.103}, {'end': 317.522, 'text': 'Can I see a sustainable career in this domain?', 'start': 314.799, 'duration': 2.723}, {'end': 318.302, 'text': 'What do you think??', 'start': 317.782, 'duration': 0.52}, {'end': 328.727, 'text': 'Alright, so guys, imagine it like this when computers were invented, not every company in the world started to use computers,', 'start': 318.803, 'duration': 9.924}, {'end': 332.35, 'text': 'but today everyone is using a computer.', 'start': 328.727, 'duration': 3.623}, {'end': 338.254, 'text': 'without a computer, you cannot imagine any company which is basically working right.', 'start': 332.35, 'duration': 5.904}], 'summary': 'Data scientists have a sustainable career; like computers, their use will become ubiquitous.', 'duration': 27.798, 'max_score': 310.456, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk310456.jpg'}], 'start': 4.554, 'title': 'Data science insights', 'summary': "Provides insights into data science career and skills, showcasing the impact of data science in various industries with examples like microsoft's accurate oscar predictions and ai-authored books.", 'chapters': [{'end': 77.55, 'start': 4.554, 'title': 'Data science career insights', 'summary': 'Provides insights into the data science career, including the design process of the data science course, based on research from resumes and job postings, confirmed by hiring managers and data science educators.', 'duration': 72.996, 'highlights': ['The course was designed based on an analysis of resumes and job postings related to data science, with skill sets confirmed by hiring managers and data science educators, ensuring its relevance to the industry.', 'The curriculum was affirmed by data science educators, providing learners with a well-structured and industry-relevant course for pursuing a career in data science.', 'Introduction to the session and the purpose of the data science course, addressing common doubts and questions about pursuing a career in data science.']}, {'end': 465.873, 'start': 77.55, 'title': 'Data science: skills, career, and future', 'summary': "Provides insights into the skills required for data science, the ease of starting a career in the field, the importance of coding, and the future prospects of data science. it emphasizes the growing importance of data science in various industries and showcases examples of its impact, such as microsoft's accurate oscar predictions and ai-authored books.", 'duration': 388.323, 'highlights': ['Data science offers sustainable career opportunities, with increasing demand for professionals as more companies adopt data science. The speaker emphasizes that the data science field is experiencing significant growth, with increasing adoption by companies, leading to a growing demand for data science professionals, making it an opportune time to start a career in this domain.', 'Importance of coding skills for data scientists, with the distinction between the level of coding expertise required for a data scientist compared to a software development engineer. The importance of coding for data scientists is highlighted, with an emphasis on the difference in coding requirements between a data scientist and a software development engineer, where a data scientist needs to understand how to manipulate data and create machine learning models using programming languages like Python or R.', "Examples of data science impact, such as Microsoft's accurate Oscar predictions and AI-authored books, showcasing the practical applications and advancements in the field. The speaker provides examples of the impact of data science, including Microsoft's use of AI to predict Oscar winners with 96% accuracy and the creation of an AI-authored book, demonstrating the practical applications and advancements in the data science field.", 'In-depth explanation of the day-to-day work of a data scientist, including the ability to formulate questions and find answers within provided data sets. The chapter delves into the responsibilities of a data scientist, highlighting the need to independently formulate questions and derive answers from given data sets, distinguishing the role from that of a statistician who typically receives questions and data and provides answers.']}], 'duration': 461.319, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk4554.jpg', 'highlights': ['The curriculum was affirmed by data science educators, providing learners with a well-structured and industry-relevant course for pursuing a career in data science.', 'The course was designed based on an analysis of resumes and job postings related to data science, with skill sets confirmed by hiring managers and data science educators, ensuring its relevance to the industry.', 'Introduction to the session and the purpose of the data science course, addressing common doubts and questions about pursuing a career in data science.', 'Importance of coding skills for data scientists, with the distinction between the level of coding expertise required for a data scientist compared to a software development engineer.', "Examples of data science impact, such as Microsoft's accurate Oscar predictions and AI-authored books, showcasing the practical applications and advancements in the field.", 'In-depth explanation of the day-to-day work of a data scientist, including the ability to formulate questions and find answers within provided data sets.', 'Data science offers sustainable career opportunities, with increasing demand for professionals as more companies adopt data science. The speaker emphasizes that the data science field is experiencing significant growth, with increasing adoption by companies, leading to a growing demand for data science professionals, making it an opportune time to start a career in this domain.']}, {'end': 2430.931, 'segs': [{'end': 1541.166, 'src': 'embed', 'start': 1512.643, 'weight': 3, 'content': [{'end': 1519.346, 'text': 'and cluster 3 consists of other similar item which are completely different from the item of cluster 1 and cluster 2..', 'start': 1512.643, 'duration': 6.703}, {'end': 1522.627, 'text': 'OK So this is what unsupervised machine learning is.', 'start': 1519.346, 'duration': 3.281}, {'end': 1527.941, 'text': 'So one of the use case of unsupervised learning is voice based personal assistant.', 'start': 1523.499, 'duration': 4.442}, {'end': 1534.943, 'text': 'So the device which you are seeing on your screen is Amazon Echo and the brain or the voice of Echo is known as Amazon Alexa.', 'start': 1528.401, 'duration': 6.542}, {'end': 1541.166, 'text': 'The device has many smart built in to do a number of tasks like playback music make the light blink.', 'start': 1535.264, 'duration': 5.902}], 'summary': 'Unsupervised machine learning is used for voice-based personal assistant like amazon echo, operated by amazon alexa.', 'duration': 28.523, 'max_score': 1512.643, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk1512643.jpg'}, {'end': 1943.568, 'src': 'embed', 'start': 1914.147, 'weight': 0, 'content': [{'end': 1920.591, 'text': "then in that case Netflix won't recommend you German movies or Chinese movies or Japanese movie.", 'start': 1914.147, 'duration': 6.444}, {'end': 1921.171, 'text': 'All right.', 'start': 1920.931, 'duration': 0.24}, {'end': 1927.676, 'text': 'The data that Netflix feed into its algorithm can be broken down into two types, implicit and explicit.', 'start': 1921.631, 'duration': 6.045}, {'end': 1932.88, 'text': 'Explicit is, for example, you give a thumbs up to the series, The Friends, so the Netflix get it.', 'start': 1927.996, 'duration': 4.884}, {'end': 1936.542, 'text': 'And implicit data is mostly the behavioral data.', 'start': 1933.64, 'duration': 2.902}, {'end': 1943.568, 'text': "It's like you didn't explicitly tell Netflix that you like Black Mirror, but you just binged on it and watched it in two nights.", 'start': 1936.843, 'duration': 6.725}], 'summary': 'Netflix uses implicit and explicit data to personalize recommendations, e.g. thumbs up for friends, binge-watching black mirror.', 'duration': 29.421, 'max_score': 1914.147, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk1914147.jpg'}, {'end': 2220.69, 'src': 'embed', 'start': 2188.602, 'weight': 1, 'content': [{'end': 2192.684, 'text': 'It then interprets this information and makes the best decisions based on it.', 'start': 2188.602, 'duration': 4.082}, {'end': 2196.567, 'text': 'So this was one of the use case of reinforcement learning.', 'start': 2193.105, 'duration': 3.462}, {'end': 2198.041, 'text': 'linear regression.', 'start': 2197.08, 'duration': 0.961}, {'end': 2203.105, 'text': 'you must, you all must, be remembering the linear equation that we have, right?', 'start': 2198.041, 'duration': 5.064}, {'end': 2207.028, 'text': 'so what was that linear equation that we used to have in our school days?', 'start': 2203.105, 'duration': 3.923}, {'end': 2211.651, 'text': 'it was something like this right, y equals mx.', 'start': 2207.028, 'duration': 4.623}, {'end': 2220.69, 'text': 'sorry, so the form was y equals mx plus c, right, so the form was y equals mx plus c.', 'start': 2211.651, 'duration': 9.039}], 'summary': 'Reinforcement learning use case and linear regression explained with y=mx+c equation.', 'duration': 32.088, 'max_score': 2188.602, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk2188602.jpg'}, {'end': 2390.516, 'src': 'embed', 'start': 2364.19, 'weight': 4, 'content': [{'end': 2369.555, 'text': 'so here it will have three variables, three individual variables x, a and c.', 'start': 2364.19, 'duration': 5.365}, {'end': 2372.258, 'text': 'right, three variables we will be having in that case.', 'start': 2369.555, 'duration': 2.703}, {'end': 2374.56, 'text': 'right, three variables will be having in that case.', 'start': 2372.258, 'duration': 2.302}, {'end': 2379.965, 'text': "so that's where this machine learning comes into picture and that's how this machine learning thing works.", 'start': 2374.56, 'duration': 5.405}, {'end': 2381.286, 'text': "okay, that's how machine learning works.", 'start': 2379.965, 'duration': 1.321}, {'end': 2385.009, 'text': "now let's see how we are going to use it for our model.", 'start': 2381.286, 'duration': 3.723}, {'end': 2390.516, 'text': "so that's why So this is called linear, because it is based on the equation of a linear line.", 'start': 2385.009, 'duration': 5.507}], 'summary': 'Introduction to machine learning with three variables and linear equations.', 'duration': 26.326, 'max_score': 2364.19, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk2364190.jpg'}], 'start': 467.296, 'title': 'Ai and data science applications', 'summary': "Introduces an ai app called 'fake faces', discusses the impact of data science on a finance company, explores machine learning applications including amazon alexa, and details netflix's use of machine learning for recommendations and reinforcement learning in self-driving cars.", 'chapters': [{'end': 509.549, 'start': 467.296, 'title': 'Ai-generated fake faces', 'summary': "Introduces an ai app called 'fake faces' created by university researchers, which can generate fake faces indistinguishable from real ones, using a database of millions of human faces and gan algorithm.", 'duration': 42.253, 'highlights': ["The app 'fake faces' created by university researchers uses GAN algorithm to generate fake faces indistinguishable from real ones, using a database of millions of human faces.", "The AI app 'fake faces' is able to create faces that look exactly like real human faces by combining different features using AI technology.", "The app 'fake faces' by university researchers can generate fake faces that are indistinguishable from real ones, addressing the issue of fake profiles on the internet."]}, {'end': 1431.296, 'start': 509.549, 'title': 'Impact of data science on business', 'summary': "Discusses how data science has impacted a finance company's productivity, leading to improved loan conversion rates and millions in savings, provides an overview of data science, and explains the process and types of machine learning algorithms.", 'duration': 921.747, 'highlights': ['Data Science Impact on Finance Company The bank implemented data science to filter potential loan customers, resulting in higher conversion rates and millions in savings from reduced manual efforts.', 'Overview of Data Science Data science is a multidisciplinary field using various technologies to extract meaningful insights from data, contrary to the misconception that only statisticians can become data scientists.', 'Difference Between Data Scientist and Statistician Data scientists proactively identify insights from data, while statisticians need specific questions to work on and implement statistical concepts.', 'Machine Learning Process Machine learning involves data preparation, training, testing, and predicting phases, with the training phase using a labeled dataset to train the machine for future identification tasks.', 'Types of Machine Learning Algorithms Supervised learning, unsupervised learning, and reinforcement learning are the three main types of machine learning algorithms, with supervised learning involving training the machine on labeled data.']}, {'end': 1735.335, 'start': 1431.757, 'title': 'Machine learning applications & amazon alexa', 'summary': 'Discusses real-time processes, supervised and unsupervised machine learning applications, and the functionality and capabilities of amazon alexa, highlighting its impact on various tasks and its integration with other services.', 'duration': 303.578, 'highlights': ["Amazon Alexa can perform a wide range of tasks such as controlling smart home devices, ordering from online services, and playing music, with integration capabilities extending to services like Uber and Domino's. Amazon Alexa's versatility enables it to control smart home devices, order from online services, and play music, with integration capabilities extending to services like Uber and Domino's.", 'Unsupervised learning involves machine identification of clusters without labels, with voice-based personal assistants being a notable use case. Unsupervised learning involves machine identification of clusters without labels, with voice-based personal assistants being a notable use case.', 'The process of fingerprint analysis involves saving the fingerprint data in a machine for verification, showcasing an application of supervised learning. The process of fingerprint analysis involves saving the fingerprint data in a machine for verification, showcasing an application of supervised learning.']}, {'end': 2430.931, 'start': 1735.355, 'title': 'Netflix recommendation & reinforcement learning', 'summary': 'Discusses how netflix uses machine learning to recommend 80% of tv shows, with over 250 million active profiles, and the application of reinforcement learning in self-driving cars, citing a 90% reduction in road accidents due to human error.', 'duration': 695.576, 'highlights': ["Netflix uses machine learning to recommend over 80% of TV shows, with over 250 million active profiles. Netflix's recommendation system influences the majority of content choices, leveraging data from 250 million active profiles.", 'Reinforcement learning has shown a 90% reduction in road accidents due to human error. Reinforcement learning in self-driving cars has potential to significantly reduce road accidents, with a reported 90% decrease attributed to human error.', "Netflix's recommendation system combines user behavior data with in-house and freelance staff-generated content tags. Netflix's algorithm combines user behavior data with content tags generated by in-house and freelance staff, contributing to personalized recommendations.", 'Self-driving cars rely on IoT sensors, connectivity, and software algorithms to operate autonomously. Self-driving cars utilize IoT sensors, connectivity, and software algorithms for navigation and decision-making, enhancing safety and reducing human error.', 'Linear regression is a machine learning technique to find relationships between variables. Linear regression is a fundamental machine learning technique used to establish relationships between variables, enabling predictive analysis.']}], 'duration': 1963.635, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk467296.jpg', 'highlights': ["The app 'fake faces' uses GAN algorithm to generate indistinguishable fake faces from a database of millions of human faces.", 'Data science implementation in a finance company resulted in higher conversion rates and millions in savings from reduced manual efforts.', "Amazon Alexa's versatility enables it to control smart home devices, order from online services, and play music, with integration capabilities extending to services like Uber and Domino's.", "Netflix's recommendation system influences the majority of content choices, leveraging data from 250 million active profiles and combining user behavior data with content tags.", 'Reinforcement learning in self-driving cars has potential to significantly reduce road accidents, with a reported 90% decrease attributed to human error.']}, {'end': 5090.647, 'segs': [{'end': 2932.671, 'src': 'embed', 'start': 2890.937, 'weight': 0, 'content': [{'end': 2895.427, 'text': 'okay, those parameters which impact the outcome, like dependent variable.', 'start': 2890.937, 'duration': 4.49}, {'end': 2902.45, 'text': "So let's say if we have 10 rows and 15 columns then 14 columns in that will be dependent independent variables.", 'start': 2895.447, 'duration': 7.003}, {'end': 2903.67, 'text': 'Those are X values.', 'start': 2902.89, 'duration': 0.78}, {'end': 2904.73, 'text': 'They can go anywhere.', 'start': 2903.71, 'duration': 1.02}, {'end': 2913.954, 'text': 'Okay, the variables, the target variables, will be the dependent variables, right when we say continuous and categorical, that is,', 'start': 2905.07, 'duration': 8.884}, {'end': 2917.735, 'text': 'that is the dependent variables, that is the Y values, that is the outcome values.', 'start': 2913.954, 'duration': 3.781}, {'end': 2920.816, 'text': 'right because independent variables can be anywhere.', 'start': 2917.735, 'duration': 3.081}, {'end': 2923.003, 'text': 'It can be anywhere in the plane.', 'start': 2921.276, 'duration': 1.727}, {'end': 2929.65, 'text': "Correct? So that's why when we consider fitting a model, we shouldn't be looking at the feature first.", 'start': 2923.264, 'duration': 6.386}, {'end': 2932.671, 'text': 'We should look at what kind of output we are expecting.', 'start': 2929.71, 'duration': 2.961}], 'summary': 'In a dataset with 10 rows and 15 columns, 14 columns are independent variables impacting the outcome. when fitting a model, the focus should be on the expected output.', 'duration': 41.734, 'max_score': 2890.937, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk2890937.jpg'}, {'end': 4176.694, 'src': 'embed', 'start': 4146.631, 'weight': 1, 'content': [{'end': 4154.895, 'text': "so that's why we won't be able to use linear set of regression, linear set of like models to fit in this kind of a requirement.", 'start': 4146.631, 'duration': 8.264}, {'end': 4158.499, 'text': 'instead, we would need this kind of a curve, right.', 'start': 4154.895, 'duration': 3.604}, {'end': 4164.404, 'text': 'see, now, this fits in perfectly, right, this fits in perfectly in the under training example.', 'start': 4158.499, 'duration': 5.905}, {'end': 4169.228, 'text': 'so this curve is again called.', 'start': 4164.404, 'duration': 4.824}, {'end': 4176.694, 'text': 'this curve is called sigmoid function, right, this X of X shift curve s shift curve.', 'start': 4169.228, 'duration': 7.466}], 'summary': 'Linear regression unsuitable; sigmoid function fits perfectly for this requirement.', 'duration': 30.063, 'max_score': 4146.631, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk4146631.jpg'}], 'start': 2430.951, 'title': 'Regression models in data analysis', 'summary': 'Discusses linear regression, logistic regression, their applications in data analysis, the distinction between continuous and categorical variables, and the limitations and benefits of using these models, emphasizing their role in predicting outcomes and handling classification problems.', 'chapters': [{'end': 3131.258, 'start': 2430.951, 'title': 'Understanding linear regression', 'summary': 'Highlights the concept of linear regression, including the interpretation of r-squared value, the relationship between variables, and the criteria for using linear regression and logistic regression in data analysis, emphasizing the importance of continuous and categorical variables.', 'duration': 700.307, 'highlights': ["Linear regression helps to understand the relationship between variables and model performance through the interpretation of R-squared value, with a low R-squared indicating a model with considerable error. The R-squared value of 0.06 indicates a poor fit, suggesting a significant error in the model's representation of the data.", 'The concept of regression as a technique to display the relationship between variables is illustrated through examples such as temperature versus jacket usage, temperature versus ice cream sales, and snowfall versus skiing park visitors. Examples include the direct relationship between temperature and jacket usage, the correlation between temperature and ice cream sales, and the impact of snowfall on the number of visitors at a skiing park.', 'The chapter explains the criteria for using linear regression, emphasizing the need for continuous variables, a good fit to the data points, and a straight-line curve representation. Criteria for using linear regression include the requirement for continuous variables, a good fit to the data points, and a straight-line curve representation of the relationship between variables.', 'The distinction between linear regression and logistic regression is outlined, highlighting their use cases based on the nature of variables and the type of output. The distinction is made between linear regression, suitable for continuous variables and regression problems, and logistic regression, appropriate for categorical variables and classification issues.', 'The importance of understanding the nature of variables, such as continuous and categorical, is emphasized in determining the appropriate regression technique for a given data set. Understanding the nature of variables, whether continuous or categorical, is critical in selecting the appropriate regression technique for a given data set.']}, {'end': 3871.463, 'start': 3131.824, 'title': 'Regression vs classification and goodness of fit', 'summary': 'Discusses the distinction between categorical and continuous variables, the selection of regression or classification models based on the type of dataset, the calculation of r square for evaluating the goodness of fit, and the steps involved in calculating the slope and intercept in linear regression.', 'duration': 739.639, 'highlights': ['The distinction between categorical and continuous variables is crucial for determining whether a regression or classification model should be employed, affecting model selection and approach.', 'The calculation of R square, using the least square method, is essential for evaluating the goodness of fit in regression models, with a smaller R square indicating a less accurate fit and a larger R square denoting a better fit.', "The detailed explanation of calculating the slope and intercept in linear regression, including the steps for finding the value of 'm' and the 'c' value, provides insights into the practical implementation of linear regression analysis."]}, {'end': 4280.681, 'start': 3871.463, 'title': 'Logistic regression basics', 'summary': 'Covers the basics of logistic regression, including the different types of machine learning, the need for logistic regression in classification problems, and the characteristics of the sigmoid function used in logistic regression, emphasizing its ability to fit skewed data points.', 'duration': 409.218, 'highlights': ['Logistic regression is used for classification problems where the data is skewed and can be categorized into two distinct values, such as 0 and 1, with typical examples including tumor prediction, spam classification, and fraudulent transaction detection. Logistic regression is specifically designed for skewed data with typical examples including tumor prediction, spam classification, and fraudulent transaction detection.', 'The sigmoid function, used in logistic regression, is an asymptotic curve that never reaches 0 or 1, asymptoting to 0 at negative infinity and 1 at positive infinity, making it suitable for fitting skewed data points in classification problems. The sigmoid function, an asymptotic curve used in logistic regression, is suitable for fitting skewed data points in classification problems as it never reaches 0 or 1, asymptoting to 0 at negative infinity and 1 at positive infinity.', 'Logistic regression is contrasted with linear regression, highlighting the need for a different approach in handling categorical problems with skewed data points. The need for logistic regression is emphasized when contrasted with linear regression, especially in handling categorical problems with skewed data points.']}, {'end': 4607.864, 'start': 4280.681, 'title': 'Logistic regression and categorical problems', 'summary': 'Covers the basics of logistic regression and provides an example of using linear regression for property size prediction and the limitations of using linear regression for categorical problems, emphasizing the need for logistic regression. it also highlights the relationship between property size and garden area, and the challenges of predicting neighborhood preferences using linear regression.', 'duration': 327.183, 'highlights': ['The chapter emphasizes the limitations of using linear regression for categorical problems and the need for logistic regression due to the skewness of categorical data, with an example demonstrating the challenges of predicting neighborhood preferences using a linear regression model.', 'The discussion includes an example of using linear regression for property size prediction, illustrating the relationship between property size and garden area and the linear equation between the two variables.', 'The chapter explains the concept of logistic regression and its basis on the sigmoid function, highlighting its use in establishing logistic regression functions and its differentiation from linear regression.', "The speaker describes the basics of logistic regression, focusing on the sigmoid function's role in replacing a linear regression line and establishing logistic regression functions based on it."]}, {'end': 5090.647, 'start': 4607.864, 'title': 'Logistic regression for classification', 'summary': 'Discusses the limitations of linear regression in solving classification problems and introduces logistic regression as a statistical classification model for categorical dependent variables, capable of handling both univariate and multinomial features, as well as continuous and discrete input data, providing outcomes in terms of probability.', 'duration': 482.783, 'highlights': ['Logistic regression is introduced as a statistical classification model for categorical dependent variables, capable of handling both univariate and multinomial features. Logistic regression is discussed as a statistical classification model that can handle both univariate and multinomial features, addressing the limitations of linear regression in solving classification problems.', 'The model provides outcomes in terms of probability, offering a probabilistic output for the prediction of problems such as spam classification. The logistic regression model provides outcomes in terms of probability, enabling predictions for problems like spam classification based on the likelihood of an email being spam.', 'It is capable of handling both continuous and discrete input data, including fitting an S-shaped curve to continuous data. Logistic regression can handle both continuous and discrete input data, including fitting an S-shaped curve to continuous data, although errors may arise due to high error rates.']}], 'duration': 2659.696, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk2430951.jpg', 'highlights': ['Linear regression helps understand the relationship between variables and model performance through the interpretation of R-squared value, with a low R-squared indicating considerable error.', 'Logistic regression is used for classification problems where the data is skewed and can be categorized into two distinct values, such as 0 and 1, with typical examples including tumor prediction, spam classification, and fraudulent transaction detection.', 'The distinction between linear regression and logistic regression is outlined, highlighting their use cases based on the nature of variables and the type of output.', 'The importance of understanding the nature of variables, such as continuous and categorical, is emphasized in determining the appropriate regression technique for a given data set.', 'The chapter emphasizes the limitations of using linear regression for categorical problems and the need for logistic regression due to the skewness of categorical data, with an example demonstrating the challenges of predicting neighborhood preferences using a linear regression model.']}, {'end': 7032.52, 'segs': [{'end': 5186.003, 'src': 'embed', 'start': 5157.432, 'weight': 0, 'content': [{'end': 5163.975, 'text': "So let's move on ahead and see some of the machine learning algorithm and what type of algorithm can deal with these kind of question.", 'start': 5157.432, 'duration': 6.543}, {'end': 5167.273, 'text': 'So on first, we have classification algorithm.', 'start': 5164.992, 'duration': 2.281}, {'end': 5171.435, 'text': 'So using classification algorithm, you can predict a category using the data.', 'start': 5167.673, 'duration': 3.762}, {'end': 5174.097, 'text': 'For example, is this person a male or female??', 'start': 5171.736, 'duration': 2.361}, {'end': 5176.818, 'text': 'Or is this male a spam or non-spam??', 'start': 5174.437, 'duration': 2.381}, {'end': 5178.279, 'text': 'So these type of question.', 'start': 5176.998, 'duration': 1.281}, {'end': 5180.32, 'text': 'it comes under classification algorithm.', 'start': 5178.279, 'duration': 2.041}, {'end': 5186.003, 'text': 'Or is it going to rain tomorrow or not? So all these type of question comes under classification algorithm.', 'start': 5180.88, 'duration': 5.123}], 'summary': 'Classification algorithm predicts categories, e.g. gender or spam, based on data.', 'duration': 28.571, 'max_score': 5157.432, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk5157432.jpg'}, {'end': 6539.702, 'src': 'embed', 'start': 6504.589, 'weight': 4, 'content': [{'end': 6507.129, 'text': "help it focus on the person's face.", 'start': 6504.589, 'duration': 2.54}, {'end': 6509.85, 'text': "And that's what the face detection is.", 'start': 6507.67, 'duration': 2.18}, {'end': 6513.731, 'text': 'Face detection and face recognition are two different things.', 'start': 6509.87, 'duration': 3.861}, {'end': 6516.331, 'text': 'Face recognition is understanding who that person is.', 'start': 6513.951, 'duration': 2.38}, {'end': 6518.311, 'text': 'So in face recognition.', 'start': 6516.811, 'duration': 1.5}, {'end': 6525.495, 'text': 'what we do is good example of it is that is the feature that facebook has deployed,', 'start': 6518.311, 'duration': 7.184}, {'end': 6539.702, 'text': "in which you upload an image and it easy and it understands whose face is in these images and then gives you the person's name around the bounding box and tags them automatically.", 'start': 6525.495, 'duration': 14.207}], 'summary': "Face detection identifies a person's face, while face recognition determines the person's identity. for instance, facebook uses face recognition to automatically tag uploaded images.", 'duration': 35.113, 'max_score': 6504.589, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk6504589.jpg'}, {'end': 6782.329, 'src': 'embed', 'start': 6753.164, 'weight': 3, 'content': [{'end': 6758.767, 'text': "So on 37 degrees, if it's higher than 37 degrees, then it takes a look at other features.", 'start': 6753.164, 'duration': 5.603}, {'end': 6762.009, 'text': "And if it's lower than 37 degrees, it takes a look at some other features.", 'start': 6759.207, 'duration': 2.802}, {'end': 6768.032, 'text': 'And doing that after looking at all the features and coming up with these rules, it figures out okay.', 'start': 6762.409, 'duration': 5.623}, {'end': 6772.854, 'text': "the final answer that I can give you is yes, it's going to rain, or yes, it's going to be sunny.", 'start': 6768.032, 'duration': 4.822}, {'end': 6775.675, 'text': 'So then we go to random forest.', 'start': 6774.275, 'duration': 1.4}, {'end': 6782.329, 'text': 'And the random forest uses multiple decision trees to increase the accuracy of the decision.', 'start': 6776.016, 'duration': 6.313}], 'summary': 'Using decision trees to predict weather with 37-degree threshold, then improving accuracy with random forest.', 'duration': 29.165, 'max_score': 6753.164, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk6753164.jpg'}, {'end': 6988.689, 'src': 'embed', 'start': 6958.417, 'weight': 2, 'content': [{'end': 6962.28, 'text': 'so with that, i have another question for you guys.', 'start': 6958.417, 'duration': 3.863}, {'end': 6968.505, 'text': "uh, hopefully, if i show it to you on the polls, you'll be able to see it, okay.", 'start': 6962.28, 'duration': 6.225}, {'end': 6970.927, 'text': 'so now i think you can see the question.', 'start': 6968.505, 'duration': 2.422}, {'end': 6977.592, 'text': 'uh, for those of you who are unaware, the question is can you build a machine learning model by only using scikit-learn and no other library?', 'start': 6970.927, 'duration': 6.665}, {'end': 6984.468, 'text': 'so if you are not using any other library other than scikit-learn, Can you build a machine learning model?', 'start': 6977.592, 'duration': 6.876}, {'end': 6988.689, 'text': 'when I say no other library?, I mean you are not able to use Python as well too.', 'start': 6984.468, 'duration': 4.221}], 'summary': 'Can you build a machine learning model using only scikit-learn and no other libraries or python?', 'duration': 30.272, 'max_score': 6958.417, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk6958417.jpg'}], 'start': 5090.687, 'title': 'Machine learning fundamentals and applications', 'summary': 'Covers machine learning basics, a hello world ml project in python, data visualization, model building, svm model for classification, and the concept of classification and regression in machine learning, with the highest accuracy achieved being 0.99. it also introduces popular classification algorithms and their applications in various industries.', 'chapters': [{'end': 5380.718, 'start': 5090.687, 'title': 'Machine learning basics', 'summary': 'Explains the basics of machine learning, including the concept of email spam probability, types of machine learning algorithms (classification, anomaly detection, clustering, regression), and the importance of end-to-end machine learning projects for beginners.', 'duration': 290.031, 'highlights': ['The chapter explains the basics of machine learning, including the concept of email spam probability. It details the concept of email spam probability and how to interpret the output based on the math.', 'Types of machine learning algorithms are discussed, including classification, anomaly detection, clustering, and regression. It elaborates on the different types of machine learning algorithms and provides examples of questions each type can answer.', 'The importance of end-to-end machine learning projects for beginners is highlighted. It emphasizes the significance of completing end-to-end machine learning projects to gain confidence and practical experience.']}, {'end': 5591.627, 'start': 5380.718, 'title': 'Hello world ml project in python', 'summary': 'Covers the step-by-step process of creating a hello world machine learning project in python using jupyter notebook, including importing libraries, loading and analyzing the dataset, and creating data visualizations.', 'duration': 210.909, 'highlights': ['The chapter covers the step-by-step process of creating a hello world machine learning project in Python using Jupyter notebook, including importing libraries, loading and analyzing the dataset, and creating data visualizations. Covers the step-by-step process of creating a hello world machine learning project in Python, including importing libraries, loading and analyzing the dataset, and creating data visualizations.', 'The dataset consists of five different columns and 150 rows. The dataset consists of five different columns and 150 rows.', 'The output shows that there are three different classes: iris setosa, iris versicolor, and iris virginica, each consisting of 50 values. The output shows that there are three different classes: iris setosa, iris versicolor, and iris virginica, each consisting of 50 values.', 'The chapter includes a univariate plot to understand each individual variable and a multivariate plot to understand the relationship between the attributes. Includes a univariate plot to understand each individual variable and a multivariate plot to understand the relationship between the attributes.', 'Importing various machine learning models from the scikit-learn library, including KNN classifier, linear discriminant analysis, Gaussian Naive Bayes, and support vector machine. Importing various machine learning models from the scikit-learn library, including KNN classifier, linear discriminant analysis, Gaussian Naive Bayes, and support vector machine.']}, {'end': 6149.047, 'start': 5591.627, 'title': 'Data visualization and model building', 'summary': 'Discusses visualizing data distribution using univariate box and whisker plots, creating a multivariate plot to check variable interactions, and building and comparing machine learning models with a focus on support vector machine achieving the highest accuracy of 0.99.', 'duration': 557.42, 'highlights': ['Support Vector Machine achieves the highest accuracy of 0.99, followed by KNN with 0.98, indicating its potential as the best model for prediction. Support Vector Machine has the highest accuracy of 0.99, indicating its potential as the best model for prediction, followed by KNN with 0.98.', 'Creation of univariate box and whisker plots and histograms aids in understanding data distribution and identifying outliers for potential data cleaning. The univariate box and whisker plots and histograms aid in understanding data distribution and identifying outliers for potential data cleaning.', 'Multivariate plot reveals high correlation and predictable relationships between input variables, indicating the dependency of sepal length, petal length, and petal width on sepal width. Multivariate plot reveals high correlation and predictable relationships between input variables, indicating the dependency of sepal length, petal length, and petal width on sepal width.', 'Tenfold cross-validation technique is used to train and test models, and the accuracy results are compared to select the best model for prediction. Tenfold cross-validation technique is used to train and test models, and the accuracy results are compared to select the best model for prediction.']}, {'end': 6351.297, 'start': 6149.047, 'title': 'Svm model for classification', 'summary': 'Demonstrates the use of support vector machine (svm) for classification, achieving an accuracy score of 0.93, and explains precision, recall, f1 score, and support in evaluating the model performance. the chapter also compares svm with knn algorithm, highlighting the difference in accuracy scores and evaluation metrics.', 'duration': 202.25, 'highlights': ['The support vector machine (SVM) achieved an accuracy score of 0.93. The SVM model demonstrated a high accuracy score, indicating its effectiveness in classification.', 'Explanation of precision, recall, F1 score, and support metrics for model evaluation. The chapter provides a detailed explanation of the metrics used for evaluating the model performance, including precision, recall, F1 score, and support.', 'Comparison of SVM with KNN algorithm, showcasing the difference in accuracy scores and evaluation metrics. The chapter compares the performance of SVM with the KNN algorithm, highlighting the differences in accuracy scores and evaluation metrics such as precision, recall, F1 score, and support.', 'Introduction to the concept of classification in machine learning and explanation of its role in supervised learning techniques. The chapter provides an introduction to classification in machine learning, explaining its role as a supervised learning technique and its use in categorizing data based on similarity.']}, {'end': 6618.559, 'start': 6351.778, 'title': 'Classification and regression in machine learning', 'summary': 'Explains the concept of classification and regression in machine learning, highlighting the use of machine learning in banking industry for fraud detection, face detection and recognition, and intrusion detection in cybersecurity.', 'duration': 266.781, 'highlights': ["Fraud detection in the banking industry is a common use case for machine learning, which involves analyzing transaction data and user's credit scores to identify potential fraudulent transactions. Machine learning is used in fraud detection in the banking industry to analyze transaction data and user's credit scores to identify potential fraudulent transactions.", "Face detection and recognition are distinct applications, with face detection involving the identification of a person's face through bounding boxes, while face recognition identifies the person and tags them automatically. Face detection involves identifying a person's face through bounding boxes, while face recognition identifies the person and tags them automatically.", 'Intrusion detection in cybersecurity involves analyzing activity logs and IP addresses to identify potential intrusions, blacklist IP addresses, and report suspicious activities to the authorities. Intrusion detection in cybersecurity involves analyzing activity logs and IP addresses to identify potential intrusions, blacklist IP addresses, and report suspicious activities to the authorities.', 'Jupyter Notebook is a useful software for bundling up code with explanations and output, but the choice of using it in machine learning is not a crucial factor as plain Python scripts would work fine as well. Jupyter Notebook is a useful software for bundling up code with explanations and output, but the choice of using it in machine learning is not a crucial factor as plain Python scripts would work fine as well.', 'Character recognition and common classification problems such as character recognition, face detection, face recognition, fraud detection, and intrusion detection are explained as key applications of machine learning. Character recognition, face detection, face recognition, fraud detection, and intrusion detection are explained as key applications of machine learning.']}, {'end': 7032.52, 'start': 6618.999, 'title': 'Popular classification algorithms', 'summary': 'Introduces popular classification algorithms including logistic regression, decision tree, random forest, naive bayes, and the role of scikit-learn as a machine learning library with predefined algorithms and functions for common operations.', 'duration': 413.521, 'highlights': ['Scikit-learn is a free, open source machine learning library that contains generic implementation of common machine learning algorithms. Scikit-learn is a free, open source machine learning library that contains generic implementation of common machine learning algorithms.', 'Logistic regression is used to make predictions about the probability of data points being in specific categories, such as in digit recognition where it calculates the probability of each data point belonging to a category. Logistic regression is used to make predictions about the probability of data points being in specific categories, such as in digit recognition where it calculates the probability of each data point belonging to a category.', 'Decision tree uses features to split data and make decisions, while random forest increases accuracy by using multiple decision trees and selecting the result with the highest frequency. Decision tree uses features to split data and make decisions, while random forest increases accuracy by using multiple decision trees and selecting the result with the highest frequency.', 'Naive Bayes is a probabilistic algorithm used to determine the probability of something mapping to a particular class. Naive Bayes is a probabilistic algorithm used to determine the probability of something mapping to a particular class.']}], 'duration': 1941.833, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk5090687.jpg', 'highlights': ['Support Vector Machine achieves the highest accuracy of 0.99, indicating its potential as the best model for prediction.', 'The chapter compares the performance of SVM with the KNN algorithm, highlighting the differences in accuracy scores and evaluation metrics such as precision, recall, F1 score, and support.', 'The chapter explains the basics of machine learning, including the concept of email spam probability.', 'The chapter provides an introduction to classification in machine learning, explaining its role as a supervised learning technique and its use in categorizing data based on similarity.', "Machine learning is used in fraud detection in the banking industry to analyze transaction data and user's credit scores to identify potential fraudulent transactions.", 'Scikit-learn is a free, open source machine learning library that contains generic implementation of common machine learning algorithms.']}, {'end': 7986.235, 'segs': [{'end': 7061.635, 'src': 'embed', 'start': 7032.52, 'weight': 2, 'content': [{'end': 7037.903, 'text': 'which is for the question that can you build a machine learning model with only using scikit-learn and no other library?', 'start': 7032.52, 'duration': 5.383}, {'end': 7039.624, 'text': 'The correct answer is yes, you can.', 'start': 7037.943, 'duration': 1.681}, {'end': 7050.028, 'text': "The people who have answered no, I'm assuming that you guys were thinking that because we have to use NumPy or Pandas or other libraries like that,", 'start': 7042.364, 'duration': 7.664}, {'end': 7055.151, 'text': 'we could probably not be able to build a model without using those libraries.', 'start': 7050.028, 'duration': 5.123}, {'end': 7061.635, 'text': 'The thing is, you can build a model without using those libraries, but you would have to do a lot of coding yourself.', 'start': 7055.992, 'duration': 5.643}], 'summary': 'You can build a machine learning model using only scikit-learn, without other libraries.', 'duration': 29.115, 'max_score': 7032.52, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk7032520.jpg'}, {'end': 7627.551, 'src': 'embed', 'start': 7598.854, 'weight': 0, 'content': [{'end': 7603.397, 'text': "We'll be using the logistic regression model, which we can import from sklearn.linear model.", 'start': 7598.854, 'duration': 4.543}, {'end': 7608.119, 'text': 'And finally, we will be taking a look at the accuracy score as well.', 'start': 7604.397, 'duration': 3.722}, {'end': 7611.982, 'text': 'And scikit-learn already gives us a function that does it for us.', 'start': 7608.26, 'duration': 3.722}, {'end': 7621.067, 'text': "I'll be more than happy to answer you on the questions that you ask about the code that you're watching.", 'start': 7612.782, 'duration': 8.285}, {'end': 7627.551, 'text': 'If you want to follow along, you can type in the code in your Jupyter notebooks if you have it.', 'start': 7621.427, 'duration': 6.124}], 'summary': 'Using logistic regression model for analysis, with a focus on accuracy score.', 'duration': 28.697, 'max_score': 7598.854, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk7598854.jpg'}, {'end': 7748.111, 'src': 'embed', 'start': 7718.972, 'weight': 1, 'content': [{'end': 7723.474, 'text': "they don't understand words or strings, or letters or sentences.", 'start': 7718.972, 'duration': 4.502}, {'end': 7730.476, 'text': 'so we need to first convert those numbers into letters in classes, into numbers, and then we can make the prediction.', 'start': 7723.474, 'duration': 7.002}, {'end': 7733.917, 'text': 'and now for our columns.', 'start': 7730.476, 'duration': 3.441}, {'end': 7738.458, 'text': 'we want to be able to understand what are the different columns that we have.', 'start': 7733.917, 'duration': 4.541}, {'end': 7744.329, 'text': 'and what we get here is we get the separate length, separate width, by the length and pattern width.', 'start': 7738.458, 'duration': 5.871}, {'end': 7748.111, 'text': "so it's given us the example of what we have.", 'start': 7744.329, 'duration': 3.782}], 'summary': 'Data needs to be converted into letters and classes for prediction. columns include separate length, separate width, and pattern width.', 'duration': 29.139, 'max_score': 7718.972, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk7718972.jpg'}], 'start': 7032.52, 'title': 'Building ml model with scikit-learn', 'summary': 'Discusses building machine learning models using only scikit-learn, emphasizing its advantages such as ease of use, extensive documentation, expertise of creators, and being well-tested and open-source. it also covers the process of importing necessary libraries, preparing the dataset, splitting the data, training the model, and testing its accuracy, highlighting the importance of data splitting and model training in achieving higher accuracy. additionally, it explains the python dictionary as a key-value pair and the logistic regression model, achieving an 88% accuracy with a 70-30 data split.', 'chapters': [{'end': 7227.344, 'start': 7032.52, 'title': 'Building ml model with scikit-learn', 'summary': "Discusses building machine learning models using only scikit-learn, emphasizing that it's possible to do so without using other libraries, but it would require a lot of coding. it also highlights the advantages of using scikit-learn, such as its ease of use, extensive documentation, expertise of creators, and being well-tested and open-source.", 'duration': 194.824, 'highlights': ["It's possible to build a machine learning model using only scikit-learn, without using other libraries, but it would require a lot of coding for tasks like importing data, parsing, and splitting. The correct answer is yes, you can build a model without using other libraries, but it would require a lot of coding for tasks like importing data, parsing, and splitting.", 'Scikit-learn is very easy to learn and use, as it contains the implementation of mathematical algorithms and is well-documented. Scikit-learn is extremely easy to learn and use, as it contains the implementation of mathematical algorithms and is well-documented.', 'The library has been created by expert data scientists and is well-tested and open-source, making it possible for developers to contribute patches, bug fixes, and new features. Scikit-learn has been created by expert data scientists, is well-tested and open-source, allowing developers to contribute patches, bug fixes, and new features.', 'There are five generic steps in building a classifier using scikit-learn, but the number of steps may vary based on the model training approach. There are five generic steps in building a classifier using scikit-learn, but the number of steps may vary based on the model training approach.']}, {'end': 7672.906, 'start': 7227.544, 'title': 'Data science: importing, training, and testing models', 'summary': "Discusses the process of importing necessary libraries, importing and preparing the dataset, splitting the data into training and testing sets, training the model using scikit-learn library, and testing the model's accuracy, emphasizing the importance of data splitting and model training in achieving higher accuracy.", 'duration': 445.362, 'highlights': ['Importing Necessary Libraries Importing all necessary libraries such as SQL, NumPy, and Panda at the top of the file is emphasized to ensure clarity and dependency understanding for the code users.', 'Data Splitting for Model Training and Testing Emphasizing the process of splitting the data into training and testing sets to train the model and assess its accuracy, with a recommended 70-30 split for small datasets and the testing set used for model performance validation.', 'Training the Model Using scikit-learn Highlighting the use of scikit-learn library for training the model by feeding in the data and emphasizing the significance of model learning from the data for making predictions.', "Model Accuracy Testing and Considerations Discussing the testing of the model's accuracy and addressing the considerations for 100% accuracy, potential overfitting, and the impact of dataset size on accuracy assessment."]}, {'end': 7986.235, 'start': 7673.566, 'title': 'Python dictionary and logistic regression model', 'summary': 'Explains the python dictionary as a key-value pair and the logistic regression model, with a 70-30 data split resulting in an 88% accuracy, which can vary due to random data splitting.', 'duration': 312.669, 'highlights': ['The chapter explains the Python dictionary as a key-value pair and the logistic regression model It introduces the Python dictionary as a common data structure and details the process of creating and training a logistic regression model.', 'a 70-30 data split resulting in an 88% accuracy The data is split into 70% for training and 30% for testing, resulting in an 88% accuracy for the logistic regression model.', 'the accuracy, which can vary due to random data splitting The accuracy of the model is noted to vary due to the random nature of the data split, with examples of 100% and 91% accuracies achieved due to the small dataset and random splitting.']}], 'duration': 953.715, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk7032520.jpg', 'highlights': ['Scikit-learn is extremely easy to learn and use, as it contains the implementation of mathematical algorithms and is well-documented.', 'The library has been created by expert data scientists, is well-tested and open-source, allowing developers to contribute patches, bug fixes, and new features.', 'Data Splitting for Model Training and Testing Emphasizing the process of splitting the data into training and testing sets to train the model and assess its accuracy, with a recommended 70-30 split for small datasets and the testing set used for model performance validation.', 'The chapter explains the Python dictionary as a key-value pair and the logistic regression model It introduces the Python dictionary as a common data structure and details the process of creating and training a logistic regression model.', 'a 70-30 data split resulting in an 88% accuracy The data is split into 70% for training and 30% for testing, resulting in an 88% accuracy for the logistic regression model.']}, {'end': 12609.164, 'segs': [{'end': 10481.2, 'src': 'embed', 'start': 10453.116, 'weight': 5, 'content': [{'end': 10455.537, 'text': 'minimum value is 1, 1 to 4.', 'start': 10453.116, 'duration': 2.421}, {'end': 10458.058, 'text': 'correct mean is 2.33.', 'start': 10455.537, 'duration': 2.521}, {'end': 10460.399, 'text': 'that is 4 plus 2 plus 1.', 'start': 10458.058, 'duration': 2.341}, {'end': 10462.719, 'text': 'that is 7 by 3.', 'start': 10460.399, 'duration': 2.32}, {'end': 10465.681, 'text': '2.33. median is 2.0.', 'start': 10462.719, 'duration': 2.962}, {'end': 10469.062, 'text': 'Correlation coefficient that is 1 and standard deviation is 1.24..', 'start': 10465.681, 'duration': 3.381}, {'end': 10473.757, 'text': 'Okay, so So this was about the aggregate function.', 'start': 10469.062, 'duration': 4.695}, {'end': 10475.237, 'text': "Let's come back.", 'start': 10474.577, 'duration': 0.66}, {'end': 10478.779, 'text': 'So next we have is numpy broadcasting.', 'start': 10475.978, 'duration': 2.801}, {'end': 10481.2, 'text': "So let's see what exactly is.", 'start': 10479.319, 'duration': 1.881}], 'summary': 'Aggregate function results: min=1, max=4, mean=2.33, median=2.0, correlation=1, stdev=1.24.', 'duration': 28.084, 'max_score': 10453.116, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk10453116.jpg'}, {'end': 10604.468, 'src': 'embed', 'start': 10572.767, 'weight': 3, 'content': [{'end': 10578.389, 'text': "okay, now, let's define another array, b, equal np dot array.", 'start': 10572.767, 'duration': 5.622}, {'end': 10583.332, 'text': "so in this let's specify a number like zero, one and two.", 'start': 10578.389, 'duration': 4.943}, {'end': 10589.495, 'text': 'so this is the number which will be added to zero, zero, zero, one, two, three, four, five, six and five, six, seven.', 'start': 10583.332, 'duration': 6.163}, {'end': 10593.283, 'text': 'and this is the concept of broadcasting.', 'start': 10590.322, 'duration': 2.961}, {'end': 10599.226, 'text': 'okay, let me just show you print first array.', 'start': 10593.283, 'duration': 5.943}, {'end': 10604.468, 'text': 'first array is what a rate.', 'start': 10599.226, 'duration': 5.242}], 'summary': 'Defining array b, broadcasting concept, and displaying the first array.', 'duration': 31.701, 'max_score': 10572.767, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk10572767.jpg'}, {'end': 11547.32, 'src': 'embed', 'start': 11507.984, 'weight': 18, 'content': [{'end': 11510.725, 'text': 'okay, now I need to compare it with numpy array.', 'start': 11507.984, 'duration': 2.741}, {'end': 11514.146, 'text': "so let's define numpy array.", 'start': 11510.725, 'duration': 3.421}, {'end': 11518.987, 'text': "so let's say a numpy array, a equals NP dot.", 'start': 11514.146, 'duration': 4.841}, {'end': 11523.249, 'text': 'arrange thousand numbers.', 'start': 11518.987, 'duration': 4.262}, {'end': 11528.068, 'text': 'okay, again, print a dot size multiplied by.', 'start': 11523.249, 'duration': 4.819}, {'end': 11533.932, 'text': 'you can directly write thousand over here or you can use a dot item size.', 'start': 11528.068, 'duration': 5.864}, {'end': 11535.553, 'text': "okay, let's execute it.", 'start': 11533.932, 'duration': 1.621}, {'end': 11547.32, 'text': "let's modify our code a bit here size of our list and here size of an array.", 'start': 11535.553, 'duration': 11.767}], 'summary': 'Comparing list size with numpy array size using 1000 numbers.', 'duration': 39.336, 'max_score': 11507.984, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk11507984.jpg'}, {'end': 12124.664, 'src': 'embed', 'start': 12094.113, 'weight': 1, 'content': [{'end': 12098.357, 'text': 'so first of all, we randomly pick two different cluster centers.', 'start': 12094.113, 'duration': 4.244}, {'end': 12100.918, 'text': "okay, Then, as a step 2, what we'll do?", 'start': 12098.357, 'duration': 2.561}, {'end': 12105.679, 'text': "we'll assign our data set into two cluster, based on minimum distance, to the cluster centers.", 'start': 12100.918, 'duration': 4.761}, {'end': 12110.26, 'text': "Fine Then we'll calculate the mean distance of each member in the cluster.", 'start': 12106.299, 'duration': 3.961}, {'end': 12115.021, 'text': "Then as a step 4, what we'll do, we'll shift the cluster center towards the mean.", 'start': 12110.92, 'duration': 4.101}, {'end': 12118.982, 'text': "And we'll consider that two mean values are the new cluster centers.", 'start': 12115.361, 'duration': 3.621}, {'end': 12124.664, 'text': "Okay Then again, we'll repeat step 2 and step 3 until no member changes the group.", 'start': 12119.663, 'duration': 5.001}], 'summary': 'Algorithm iteratively shifts cluster centers based on mean distance until convergence', 'duration': 30.551, 'max_score': 12094.113, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk12094113.jpg'}, {'end': 12155.163, 'src': 'embed', 'start': 12132.286, 'weight': 0, 'content': [{'end': 12139.931, 'text': "so let's say we have a data set in which we have coordinates as x, y, distance, k1, distance k2, minimum and cluster.", 'start': 12132.286, 'duration': 7.645}, {'end': 12148.298, 'text': 'so these are our attributes over there distance k1 is the distance between the point and the centroid of cluster 1,', 'start': 12139.931, 'duration': 8.367}, {'end': 12155.163, 'text': 'and distance k2 is the distance between the point and the centroid of cluster 2..', 'start': 12148.298, 'duration': 6.865}], 'summary': 'Data set with x, y, distance, k1, k2, minimum, and cluster attributes for clustering analysis.', 'duration': 22.877, 'max_score': 12132.286, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk12132286.jpg'}, {'end': 12253.802, 'src': 'embed', 'start': 12200.852, 'weight': 7, 'content': [{'end': 12210.397, 'text': "so this also belongs to cluster k1 and similarly we'll find the distance for rest of the point and based on minimum distance we'll put them into the different cluster.", 'start': 12200.852, 'duration': 9.545}, {'end': 12217.841, 'text': 'okay. so now, after assigning the cluster, it looks something like this so cluster 1 has the point with coordinates 1, 1, 2, 2 and 4,', 'start': 12210.397, 'duration': 7.444}, {'end': 12222.903, 'text': '4 and cluster 2 have the point with coordinates 5, 5 and 6, 6.', 'start': 12217.841, 'duration': 5.062}, {'end': 12226.507, 'text': 'So our next task would be to find the mean of all the member in a cluster.', 'start': 12222.904, 'duration': 3.603}, {'end': 12238.015, 'text': 'So, the coordinate of mean of cluster 1 is 2.33, 2.33 and the coordinate of mean of cluster 2 is 5.5 and 5.5.', 'start': 12227.628, 'duration': 10.387}, {'end': 12246.359, 'text': 'okay, so now consider the coordinate 2.33 comma, 2.33 and 5.5 comma 5.5 as new k1 and k2.', 'start': 12238.015, 'duration': 8.344}, {'end': 12253.802, 'text': 'okay, again, find the distance of each point from k1 and k2, then, based on minimum distance, put them in clusters.', 'start': 12246.359, 'duration': 7.443}], 'summary': 'Using k-means clustering, points are assigned to clusters and mean coordinates are recalculated for further clustering.', 'duration': 52.95, 'max_score': 12200.852, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk12200852.jpg'}, {'end': 12516.949, 'src': 'embed', 'start': 12489.467, 'weight': 13, 'content': [{'end': 12494.59, 'text': 'So first of all, we are importing the numpy library and we are storing all these data in the numpy array.', 'start': 12489.467, 'duration': 5.123}, {'end': 12500.013, 'text': 'Okay, so copy equal np.array and inside that we are passing all these values of sugar.', 'start': 12495.17, 'duration': 4.843}, {'end': 12503.836, 'text': 'Okay, next what we are doing, we are printing the mean and stamp deviation of it.', 'start': 12500.233, 'duration': 3.603}, {'end': 12511.007, 'text': 'So as you can see, we got a mean of 21.5 and a stamp deviation was 8.06.', 'start': 12504.316, 'duration': 6.691}, {'end': 12513.808, 'text': 'Okay Then what we did, we created a plot out of it.', 'start': 12511.007, 'duration': 2.801}, {'end': 12516.949, 'text': 'So for creating a plot, I used a matplotlib over here.', 'start': 12513.948, 'duration': 3.001}], 'summary': 'Imported data into numpy array, calculated mean (21.5) and standard deviation (8.06), and created a plot using matplotlib.', 'duration': 27.482, 'max_score': 12489.467, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk12489467.jpg'}, {'end': 12584.193, 'src': 'embed', 'start': 12532.754, 'weight': 2, 'content': [{'end': 12538.555, 'text': "Now let's say a grand mocha cappuccino at the coffee shop contains 14 grams of sugar.", 'start': 12532.754, 'duration': 5.801}, {'end': 12545.184, 'text': 'to calculate this standardized score or z score for the grande moja cappuccino, so how will we do that?', 'start': 12538.555, 'duration': 6.629}, {'end': 12549.606, 'text': 'so for calculating the z score, uh, so you have to import stats.', 'start': 12545.184, 'duration': 4.422}, {'end': 12552.026, 'text': "so from scipy i'm importing stats.", 'start': 12549.606, 'duration': 2.42}, {'end': 12555.567, 'text': 'then print stats dot z score coffee.', 'start': 12552.026, 'duration': 3.541}, {'end': 12560.349, 'text': 'okay. so here we got the list of different z scores for different values.', 'start': 12555.567, 'duration': 4.782}, {'end': 12565.39, 'text': 'okay, so so the z score of a 14 gram sugar coffee is minus or 0.93.', 'start': 12560.349, 'duration': 5.041}, {'end': 12569.47, 'text': 'right, So, but what does this mean?', 'start': 12565.39, 'duration': 4.08}, {'end': 12570.651, 'text': 'So any guess anyone?', 'start': 12569.69, 'duration': 0.961}, {'end': 12571.911, 'text': 'What do you think it means??', 'start': 12571.031, 'duration': 0.88}, {'end': 12576.091, 'text': 'This drink has 0.9 gram of sugar less than the mean of the 13 drinks.', 'start': 12572.051, 'duration': 4.04}, {'end': 12578.592, 'text': "Or it's B.", 'start': 12576.692, 'duration': 1.9}, {'end': 12582.353, 'text': 'This drink is 0.9 standard deviation below the mean of the 13 drinks.', 'start': 12578.592, 'duration': 3.761}, {'end': 12584.193, 'text': "Or it's C.", 'start': 12582.853, 'duration': 1.34}], 'summary': "A grand mocha cappuccino at the coffee shop contains 14 grams of sugar, with a z-score of -0.93 indicating it's 0.9 standard deviation below the mean.", 'duration': 51.439, 'max_score': 12532.754, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk12532754.jpg'}], 'start': 7987.116, 'title': 'Numpy and scipy in python', 'summary': 'Introduces numpy for array creation, initialization, mathematics, and manipulation, highlighting its benefits over lists. it also explores scipy for clustering, stats, and statistical functions, emphasizing its efficiency and usefulness in scientific computing.', 'chapters': [{'end': 8167.206, 'start': 7987.116, 'title': 'Introduction to numpy and array creation', 'summary': "Introduces numpy as the most widely used python library for linear algebra, explaining its functions and demonstrating the creation of 1d and 2d arrays, with an emphasis on using 'np' as an alias for 'numpy' to simplify function calls, and addressing the error in creating a 2d array due to incorrect array format.", 'duration': 180.09, 'highlights': ['NumPy is the most widely used Python library for linear algebra. NumPy is the most widely used Python library for linear algebra, facilitating mathematical and logical operations on multi-dimensional arrays.', "Demonstration of creating 1D and 2D arrays with examples. The chapter provides examples of creating 1D and 2D arrays, demonstrating the process and emphasizing the use of 'np' as an alias for 'numpy' to simplify function calls.", 'Explanation of the error in creating a 2D array due to incorrect array format. The chapter addresses the error in creating a 2D array, attributed to passing a list instead of a list of arrays, providing clarification and guidance for correct array format.']}, {'end': 9493.551, 'start': 8167.206, 'title': 'Initializing and inspecting numpy arrays', 'summary': "Covers the methods for initializing a numpy array including initializing with zero values, arranging numbers with intervals, filling with the same number and random numbers, and inspecting the array's shape, dimension, and number of elements.", 'duration': 1326.345, 'highlights': ['Initializing arrays with predefined functions like np.zeros and np.arange allows for efficient creation of arrays with specific dimensions and values. The function np.zeros is used to initialize all the elements of an array to zero, which can be specified with a list of dimensions. For example, initializing a 3x4 array with all elements as 0 results in a 3x4 matrix with all elements initialized to 0.', 'Using the np.arange function allows for the arrangement of numbers between specified intervals, such as printing all the numbers between 1 to 10 with an interval of 2. The np.arange function allows for arranging numbers between specified start and end points with a specific interval. For example, arranging numbers between 1 to 10 with an interval of 2 results in the output 1, 3, 5, 7, 9.', 'The np.linspace function is employed to arrange a specific number of values between given start and end points, such as arranging 10 numbers between 5 and 10. The np.linspace function is used to arrange a specified number of values between given start and end points. For instance, arranging 10 numbers between 5 and 10 results in an array of 10 numbers between 5 and 10.', 'The np.full function facilitates filling an array with the same specified number, allowing for the creation of arrays with all elements initialized to the given value. The np.full function takes the dimension of the array and the element to be filled in as parameters, allowing for the creation of arrays with all elements initialized to the specified value. For instance, creating a 2x3 array with all values initialized to 6 results in a 2x3 array with all elements as 6.', 'The inspection of NumPy arrays involves functions such as shape, ndim, and size, which provide information about the dimensions, shape, and number of elements in the array. Functions such as shape, ndim, and size are used for inspecting NumPy arrays, providing information about the dimensions, shape, and number of elements in the array. For example, the shape function returns a tuple consisting of the array dimensions and can be used to resize the array, while the size function returns the total number of elements in the array.']}, {'end': 10331.776, 'start': 9493.551, 'title': 'Numpy array mathematics', 'summary': 'Covers numpy array inspection and mathematics, including counting elements, finding data types, and performing mathematical functions like addition, subtraction, multiplication, division, exponentiation, square root, sine, cosine, and logarithm. it also explains array comparison, both element-wise and array-wise.', 'duration': 838.225, 'highlights': ['The chapter covers NumPy array inspection and mathematics, including counting elements, finding data types, and performing mathematical functions like addition, subtraction, multiplication, division, exponentiation, square root, sine, cosine, and logarithm. Covers various aspects of NumPy array inspection and mathematics, including counting elements, data type identification, and various mathematical functions.', 'Explains array comparison, both element-wise and array-wise. Demonstrates element-wise and array-wise comparison of arrays, showcasing how to determine if elements or entire arrays are equal.']}, {'end': 11410.553, 'start': 10332.776, 'title': 'Python array manipulation', 'summary': 'Covers numpy aggregate function, broadcasting, array manipulation, concatenating and stacking arrays, splitting arrays, and indexing and slicing in python.', 'duration': 1077.777, 'highlights': ['The chapter covers numpy aggregate function which includes performing various statistical calculations on the array such as sum, minimum value, mean, median, correlation coefficient, and standard deviation. Various statistical calculations on the array such as sum, minimum value, mean, median, correlation coefficient, and standard deviation.', 'The chapter explains numpy broadcasting, which involves adding arrays with different dimensions as long as the number of rows or columns are the same. Explanation of numpy broadcasting and addition of arrays with different dimensions.', 'The section discusses array manipulation in Python, covering concatenating two arrays, stacking arrays row-wise and column-wise, and combining column-wise stacked arrays. Array manipulation concepts including concatenating arrays, stacking arrays row-wise and column-wise, and combining column-wise stacked arrays.', "The chapter details splitting arrays, demonstrating horizontal and vertical splits using numpy's split function. Demonstration of horizontal and vertical splits using numpy's split function.", 'The section explores indexing and slicing in Python, explaining how to select specific elements from an array based on index and position. Explanation of indexing and slicing in Python and how to select specific elements from an array based on index and position.']}, {'end': 11985.487, 'start': 11410.553, 'title': 'Benefits of using numpy arrays', 'summary': 'Discusses the benefits of using numpy arrays over lists, highlighting that numpy arrays consume less memory and are faster and more convenient, demonstrated through examples and comparisons. additionally, the characteristics of scipy are explored, emphasizing its efficiency and usefulness in scientific computing.', 'duration': 574.934, 'highlights': ['NumPy arrays consume less memory compared to lists, with the size of a NumPy array being 4000 and a list being 28000, demonstrating a significant memory advantage. The size of a NumPy array is 4000 and the size of a list is 28000, highlighting the substantial difference in memory consumption.', 'NumPy arrays are almost 2x faster than lists, with processing times of 0.000099 for NumPy and 0.00199 for lists, showcasing the speed advantage of NumPy arrays. The processing time for NumPy arrays is 0.000099, while for lists it is 0.00199, indicating that NumPy arrays are almost 2x faster.', 'SciPy is a free and open source Python library that offers modules for optimization, linear algebra, integration, interpolation, and other scientific computations, providing efficient tools for science and engineering. SciPy provides modules for optimization, linear algebra, integration, interpolation, and more, offering efficient tools for scientific and technical computing.']}, {'end': 12609.164, 'start': 11985.487, 'title': 'Scipy clustering and stats overview', 'summary': 'Introduces the concepts of clustering and stats in scipy, explaining clustering as a process of grouping similar items into clusters and detailing the k-means algorithm for finding clusters. it also demonstrates the use of scipy to perform clustering operations and data whitening. additionally, it explores scipy.stats, showcasing various statistical functions and probability distributions, and demonstrates how to calculate z-scores for a given dataset.', 'duration': 623.677, 'highlights': ['The chapter introduces the concepts of clustering and stats in Scipy, explaining clustering as a process of grouping similar items into clusters and detailing the K-means algorithm for finding clusters. Introduces clustering and stats in Scipy, explains clustering as grouping similar items, details K-means algorithm for finding clusters.', 'It also demonstrates the use of Scipy to perform clustering operations and data whitening. Demonstrates the use of Scipy for clustering operations and data whitening.', 'Additionally, it explores Scipy.stats, showcasing various statistical functions and probability distributions, and demonstrates how to calculate z-scores for a given dataset. Explores Scipy.stats, showcases statistical functions and probability distributions, demonstrates z-score calculation.']}], 'duration': 4622.048, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk7987116.jpg', 'highlights': ['NumPy is the most widely used Python library for linear algebra, facilitating mathematical and logical operations on multi-dimensional arrays.', "Demonstration of creating 1D and 2D arrays with examples, emphasizing the use of 'np' as an alias for 'numpy' to simplify function calls.", 'Explanation of the error in creating a 2D array due to incorrect array format, providing clarification and guidance for correct array format.', 'Initializing arrays with predefined functions like np.zeros and np.arange allows for efficient creation of arrays with specific dimensions and values.', 'Using the np.arange function allows for the arrangement of numbers between specified intervals, such as printing all the numbers between 1 to 10 with an interval of 2.', 'The np.linspace function is employed to arrange a specific number of values between given start and end points, such as arranging 10 numbers between 5 and 10.', 'The np.full function facilitates filling an array with the same specified number, allowing for the creation of arrays with all elements initialized to the given value.', 'The inspection of NumPy arrays involves functions such as shape, ndim, and size, which provide information about the dimensions, shape, and number of elements in the array.', 'Covers various aspects of NumPy array inspection and mathematics, including counting elements, data type identification, and various mathematical functions.', 'Explains array comparison, both element-wise and array-wise, demonstrating how to determine if elements or entire arrays are equal.', 'Covers numpy aggregate function which includes performing various statistical calculations on the array such as sum, minimum value, mean, median, correlation coefficient, and standard deviation.', 'Explanation of numpy broadcasting and addition of arrays with different dimensions.', 'Array manipulation concepts including concatenating arrays, stacking arrays row-wise and column-wise, and combining column-wise stacked arrays.', "Demonstration of horizontal and vertical splits using numpy's split function.", 'Explanation of indexing and slicing in Python and how to select specific elements from an array based on index and position.', 'The size of a NumPy array is 4000 and the size of a list is 28000, highlighting the substantial difference in memory consumption.', 'The processing time for NumPy arrays is 0.000099, while for lists it is 0.00199, indicating that NumPy arrays are almost 2x faster.', 'SciPy provides modules for optimization, linear algebra, integration, interpolation, and more, offering efficient tools for scientific and technical computing.', 'Introduces clustering and stats in Scipy, explains clustering as grouping similar items, details K-means algorithm for finding clusters.', 'Demonstrates the use of Scipy for clustering operations and data whitening.', 'Explores Scipy.stats, showcases statistical functions and probability distributions, demonstrates z-score calculation.']}, {'end': 13544.622, 'segs': [{'end': 12681.204, 'src': 'embed', 'start': 12653.998, 'weight': 2, 'content': [{'end': 12659.442, 'text': 'But how to define whether one variable is related to other or not based on its p-value.', 'start': 12653.998, 'duration': 5.444}, {'end': 12661.464, 'text': 'So there should be one cut off right.', 'start': 12659.723, 'duration': 1.741}, {'end': 12666.457, 'text': 'So by convention, the cutoff point for a p value is 0.05.', 'start': 12661.795, 'duration': 4.662}, {'end': 12670.519, 'text': 'So if p value is less than 0.05, we can say that they are related.', 'start': 12666.457, 'duration': 4.062}, {'end': 12673.781, 'text': "But if it's greater than 0.05, they are not related.", 'start': 12670.799, 'duration': 2.982}, {'end': 12675.742, 'text': "Okay, so let's go ahead and find out.", 'start': 12673.961, 'duration': 1.781}, {'end': 12681.204, 'text': 'So we imported the numpy library created the data, then we imported the stat from scipy.', 'start': 12676.222, 'duration': 4.982}], 'summary': 'P-value cutoff of 0.05 determines variable relationship in statistical analysis.', 'duration': 27.206, 'max_score': 12653.998, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk12653998.jpg'}, {'end': 12895.659, 'src': 'embed', 'start': 12849.973, 'weight': 0, 'content': [{'end': 12851.294, 'text': 'Okay These dots over here.', 'start': 12849.973, 'duration': 1.321}, {'end': 12855.416, 'text': 'And then finally, after doing the PLT dot show, we get this thing.', 'start': 12852.055, 'duration': 3.361}, {'end': 12860.199, 'text': 'Okay So this was about how you can use SciPy dot signal to resample your data.', 'start': 12855.957, 'duration': 4.242}, {'end': 12862.621, 'text': 'Okay SciPy dot optimize.', 'start': 12860.86, 'duration': 1.761}, {'end': 12868.284, 'text': 'So in this session, you will learn about why do we use SciPy dot optimize and how do we use it?', 'start': 12862.861, 'duration': 5.423}, {'end': 12871.026, 'text': 'So why do we use scipy.optimize??', 'start': 12868.925, 'duration': 2.101}, {'end': 12875.628, 'text': 'Well, scipy.optimize provides algorithm for function minimization.', 'start': 12871.586, 'duration': 4.042}, {'end': 12878.43, 'text': 'Either it can be a scalar or multidimensional.', 'start': 12876.349, 'duration': 2.081}, {'end': 12881.892, 'text': 'You can also use it for curve fitting or root finding.', 'start': 12879.29, 'duration': 2.602}, {'end': 12885.794, 'text': 'So let us discuss how can we do that with the help of an example.', 'start': 12882.492, 'duration': 3.302}, {'end': 12889.756, 'text': "So here I'm importing matplotlib library and numpy library.", 'start': 12886.414, 'duration': 3.342}, {'end': 12895.659, 'text': "I'm using numpy library to create my dataset and matplotlib to plot this particular graph.", 'start': 12889.776, 'duration': 5.883}], 'summary': 'Learn about using scipy.optimize for function minimization and curve fitting.', 'duration': 45.686, 'max_score': 12849.973, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk12849973.jpg'}, {'end': 13026.417, 'src': 'embed', 'start': 12997.423, 'weight': 4, 'content': [{'end': 13003.609, 'text': 'Okay So here we have something called quad for single integration, DBL quad for double integration.', 'start': 12997.423, 'duration': 6.186}, {'end': 13008.312, 'text': 'TPL quad for triple integration and n quad for n-fold multiple integration.', 'start': 13003.971, 'duration': 4.341}, {'end': 13010.033, 'text': "Let's see how these things work.", 'start': 13008.752, 'duration': 1.281}, {'end': 13013.234, 'text': "Now let's move ahead and let me just show you an example.", 'start': 13010.573, 'duration': 2.661}, {'end': 13026.417, 'text': 'So if function of x equal x square where y equal f of x then find the value of integration of a to b y of dx where a equals 0 and b equals 1.', 'start': 13013.534, 'duration': 12.883}], 'summary': 'Explanation of various types of integration and an example of finding the value of a specific integral.', 'duration': 28.994, 'max_score': 12997.423, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk12997423.jpg'}, {'end': 13145.281, 'src': 'embed', 'start': 13118.548, 'weight': 1, 'content': [{'end': 13121.93, 'text': 'That is 1 by 3 minus value of A is 0 itself.', 'start': 13118.548, 'duration': 3.382}, {'end': 13123.031, 'text': 'So it will be 0.', 'start': 13121.99, 'duration': 1.041}, {'end': 13129.194, 'text': 'So 1 by 3 equals to 0.333, okay? So this we calculated it mathematically.', 'start': 13123.031, 'duration': 6.163}, {'end': 13134.838, 'text': 'So now let us see how sci-fi.integrate function work and make things easy for us.', 'start': 13129.435, 'duration': 5.403}, {'end': 13140.081, 'text': "So here the very first thing that I'll do is import the integrate library from the sci-fi.", 'start': 13135.138, 'duration': 4.943}, {'end': 13142.463, 'text': "Then I'll define a function, integrate.", 'start': 13140.222, 'duration': 2.241}, {'end': 13145.281, 'text': 'returns the value of x square.', 'start': 13143.039, 'duration': 2.242}], 'summary': '1/3 - a = 0, 1/3 = 0.333. import integrate library, define function to return x square.', 'duration': 26.733, 'max_score': 13118.548, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk13118548.jpg'}, {'end': 13272.916, 'src': 'embed', 'start': 13245.233, 'weight': 6, 'content': [{'end': 13248.674, 'text': 'So, in order to work with our fast Fourier transformation sub package,', 'start': 13245.233, 'duration': 3.441}, {'end': 13253.175, 'text': 'the very first thing that we are going to do is generate one digital signal which has some noise in it.', 'start': 13248.674, 'duration': 4.501}, {'end': 13257.136, 'text': 'Okay, so we can easily do that using NumPy library.', 'start': 13253.815, 'duration': 3.321}, {'end': 13259.756, 'text': "Okay, so let's see what we have done here.", 'start': 13257.716, 'duration': 2.04}, {'end': 13264.038, 'text': "So here the very first thing that I'm doing up is importing my NumPy library.", 'start': 13260.037, 'duration': 4.001}, {'end': 13270.199, 'text': "I'm defining a time step as 0.02, as I need to have equal number of spaces over here right.", 'start': 13264.038, 'duration': 6.161}, {'end': 13272.916, 'text': 'You can directly put this value over here.', 'start': 13270.714, 'duration': 2.202}], 'summary': 'Using numpy library to generate a digital signal with noise.', 'duration': 27.683, 'max_score': 13245.233, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk13245233.jpg'}], 'start': 12609.545, 'title': 'Using scipy for statistical tests, optimization, and fft', 'summary': 'Covers the usage of scipy.stats for g-square test with a p-value of 0.482, scipy for optimization with an integration value of 0.333, and scipy.fftpack for fast fourier transformations and noise removal.', 'chapters': [{'end': 12849.653, 'start': 12609.545, 'title': 'Using scipy.stats for g-square test', 'summary': 'Explains how to use scipy.stats to perform a g-square test to determine the dependency between frequency of going to the gym and frequency of smoking, resulting in a p-value of 0.482, indicating no dependency, and also introduces the usage of scipy.signal for signal processing with resampling using fft.', 'duration': 240.108, 'highlights': ['Performing a G-square test to determine dependency between frequency of going to the gym and frequency of smoking resulted in a p-value of 0.482, indicating no dependency based on the convention that a p-value greater than 0.05 implies no relationship. The G-square test was used to analyze the relationship between the frequency of going to the gym and the frequency of smoking, resulting in a p-value of 0.482, which is greater than 0.05, indicating no dependency based on the convention.', 'Introducing the usage of scipy.signal for typical signal processing and resampling one-dimensional or periodic signals using Fast Fourier Transformation (FFT). The usage of scipy.signal for typical signal processing, including resampling one-dimensional or periodic signals using FFT, was introduced for signal processing applications.', 'Demonstrating the process of resampling a signal with 200 data points to 100 data points using the signal.resample function and plotting the original and resampled signals for visualization. The process of resampling a signal with 200 data points to 100 data points using signal.resample function and plotting the original and resampled signals for visualization was demonstrated.']}, {'end': 13166.58, 'start': 12849.973, 'title': 'Using scipy for optimization and integration', 'summary': 'Introduces the use of scipy for optimization using optimize.minimize to find the minimum value of a function and for integration using quad for numerical integration, with a specific example resulting in an integration value of 0.333.', 'duration': 316.607, 'highlights': ['The chapter introduces the use of SciPy for optimization using optimize.minimize to find the minimum value of a function. It explains how SciPy.optimize provides algorithms for function minimization, including scalar or multidimensional minimization, curve fitting, and root finding.', 'The chapter discusses the use of quad for numerical integration in SciPy, with a specific example resulting in an integration value of 0.333. It explains the usage of quad for single integration and provides a specific example with the function f(x) = x^2 and limits a=0 and b=1, resulting in an integration value of 0.333.']}, {'end': 13544.622, 'start': 13166.58, 'title': 'Using scipy.fftpack for fast fourier transform', 'summary': 'Introduces the usage of scipy.fftpack for fast fourier transformations, including the functions fft, freq, and ifft, and demonstrates the process of generating a digital signal with noise, applying fast fourier transformation, and obtaining a noiseless signal through inverse fourier transformation.', 'duration': 378.042, 'highlights': ['The sub-package SciPy.FFTPack is used to compute fast Fourier transformations, including functions like FFT, Freq, and IFFT. The sub-package SciPy.FFTPack is used for fast Fourier transformations, providing functions such as FFT, Freq, and IFFT.', 'Demonstration of generating a digital signal with noise using NumPy library, applying fast Fourier transformation, and obtaining a noiseless signal through inverse Fourier transformation. The process involves generating a digital signal with noise using NumPy, applying fast Fourier transformation, and obtaining a noiseless signal through inverse Fourier transformation.', 'The process of applying fast Fourier transformation, filtering out sample frequencies, and plotting the original and filtered signals. The process involves applying fast Fourier transformation, filtering out sample frequencies, and plotting the original and filtered signals.']}], 'duration': 935.077, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk12609545.jpg', 'highlights': ['Performed G-square test resulted in p-value of 0.482, indicating no dependency.', 'Introduced usage of scipy.signal for signal processing and resampling using FFT.', 'Demonstrated resampling a signal with 200 data points to 100 data points.', 'Introduced SciPy for optimization using optimize.minimize to find minimum value.', 'Discussed usage of quad for numerical integration in SciPy, resulting in integration value of 0.333.', 'SciPy.FFTPack used for fast Fourier transformations, including functions like FFT, Freq, and IFFT.', 'Demonstrated generating a digital signal with noise, applying FFT, and obtaining a noiseless signal.', 'Applied FFT, filtered out sample frequencies, and plotted original and filtered signals.']}, {'end': 16119.105, 'segs': [{'end': 13630.563, 'src': 'embed', 'start': 13602.721, 'weight': 17, 'content': [{'end': 13606.126, 'text': 'okay. so this is the guy who created pandas.', 'start': 13602.721, 'duration': 3.405}, {'end': 13608.649, 'text': "now let's move ahead and see some of the features of panda.", 'start': 13606.126, 'duration': 2.523}, {'end': 13612.653, 'text': 'so on number one we have is series object and data frame.', 'start': 13608.649, 'duration': 4.004}, {'end': 13616.435, 'text': 'well, this is the primary and one of the most important features of panda.', 'start': 13612.653, 'duration': 3.782}, {'end': 13622.977, 'text': 'it allows it to deal with one-dimensional and two-dimensional label data in the form of series object and data frame.', 'start': 13616.435, 'duration': 6.542}, {'end': 13626.039, 'text': "don't worry, we'll discuss about them in detail later in our session.", 'start': 13622.977, 'duration': 3.062}, {'end': 13630.563, 'text': 'okay, next, on number two we have is handling of missing data.', 'start': 13626.039, 'duration': 4.524}], 'summary': 'Pandas creator discusses features: series, data frame, and handling missing data.', 'duration': 27.842, 'max_score': 13602.721, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk13602721.jpg'}, {'end': 13835.769, 'src': 'embed', 'start': 13789.015, 'weight': 0, 'content': [{'end': 13793.317, 'text': 'okay, so in working with large real-world data, you would need panda for computation.', 'start': 13789.015, 'duration': 4.302}, {'end': 13803.662, 'text': 'okay, next we have is panda series object is more flexible as you can use it to define your own label, index to index and access elements of an array.', 'start': 13793.317, 'duration': 10.345}, {'end': 13808.724, 'text': 'on the other hand, element and numpy arrays are accessed by the default integer position correct.', 'start': 13803.662, 'duration': 5.062}, {'end': 13810.925, 'text': 'so this is how panda is different from numpy.', 'start': 13808.724, 'duration': 2.201}, {'end': 13818.095, 'text': "So now that you know how Pandas differ from NumPy, next we'll see how we can import Pandas in Python.", 'start': 13811.57, 'duration': 6.525}, {'end': 13822.779, 'text': 'So for that, we have a command import pandas as pd.', 'start': 13819.396, 'duration': 3.383}, {'end': 13823.559, 'text': "That's it.", 'start': 13823.179, 'duration': 0.38}, {'end': 13832.886, 'text': 'Okay Next is what kind of data does suit pandas the most? So we have tabular data, time series data and arbitrary matrix data.', 'start': 13824.12, 'duration': 8.766}, {'end': 13835.769, 'text': 'All of these kind of data you can feed it to the pandas.', 'start': 13833.127, 'duration': 2.642}], 'summary': 'Pandas is suitable for tabular, time series, and matrix data; accessed with import pandas as pd.', 'duration': 46.754, 'max_score': 13789.015, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk13789015.jpg'}, {'end': 14600.22, 'src': 'embed', 'start': 14567.634, 'weight': 15, 'content': [{'end': 14573.296, 'text': 'so data frame DF, equal PD dot data frame.', 'start': 14567.634, 'duration': 5.662}, {'end': 14578.05, 'text': "and so let's add a name of the employees over here.", 'start': 14573.296, 'duration': 4.754}, {'end': 14585.677, 'text': 'so name, and this will fetch data from numpy array with cell for cell.', 'start': 14578.05, 'duration': 7.627}, {'end': 14589.049, 'text': "okay. and next let's define salary.", 'start': 14585.677, 'duration': 3.372}, {'end': 14594.795, 'text': 'so salary will fetch data from numpy array, 0th index.', 'start': 14589.049, 'duration': 5.746}, {'end': 14597.397, 'text': 'right, so my data frame is created.', 'start': 14594.795, 'duration': 2.602}, {'end': 14600.22, 'text': "now let's just print it, execute it.", 'start': 14597.397, 'duration': 2.823}], 'summary': 'Created a data frame with employee names and salaries fetched from numpy arrays.', 'duration': 32.586, 'max_score': 14567.634, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk14567634.jpg'}, {'end': 14728.542, 'src': 'embed', 'start': 14663.638, 'weight': 4, 'content': [{'end': 14669.04, 'text': 'okay. so for this, the very first thing that i need to do is create my data frames.', 'start': 14663.638, 'duration': 5.402}, {'end': 14671.761, 'text': "so first of all, let's import the libraries.", 'start': 14669.04, 'duration': 2.721}, {'end': 14675.183, 'text': 'so import pandas as pd.', 'start': 14671.761, 'duration': 3.422}, {'end': 14677.044, 'text': "i'll be needing this library only.", 'start': 14675.183, 'duration': 1.861}, {'end': 14681.385, 'text': "and now let me just define two data frames now, once i've imported the library.", 'start': 14677.044, 'duration': 4.341}, {'end': 14684.607, 'text': "so next thing i'll do is define fuel list over here.", 'start': 14681.385, 'duration': 3.222}, {'end': 14691.81, 'text': "so let's say player equal player one, player two and player three.", 'start': 14684.607, 'duration': 7.203}, {'end': 14695.375, 'text': "okay, next, let's define a list of points.", 'start': 14691.81, 'duration': 3.565}, {'end': 14697.596, 'text': 'so point equal.', 'start': 14695.375, 'duration': 2.221}, {'end': 14701.097, 'text': "let's say eight, nine, six.", 'start': 14697.596, 'duration': 3.501}, {'end': 14704.579, 'text': "okay, let's also add a list of title to it.", 'start': 14701.097, 'duration': 3.482}, {'end': 14714.323, 'text': 'so title equal, game one, game two and game three.', 'start': 14704.579, 'duration': 9.744}, {'end': 14718.424, 'text': 'so now that i have defined three list over here, i need to create a data frame out of it.', 'start': 14714.323, 'duration': 4.101}, {'end': 14725.041, 'text': "so let's say my first data frame equal pd dot data frame.", 'start': 14718.424, 'duration': 6.617}, {'end': 14727.582, 'text': "and inside this let's create a dictionary.", 'start': 14725.041, 'duration': 2.541}, {'end': 14728.542, 'text': "let's add a label.", 'start': 14727.582, 'duration': 0.96}], 'summary': 'Creating data frames using pandas library, defining lists for players, points, and titles, and creating a dictionary for the first data frame.', 'duration': 64.904, 'max_score': 14663.638, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk14663638.jpg'}, {'end': 14836.956, 'src': 'embed', 'start': 14799.161, 'weight': 5, 'content': [{'end': 14801.122, 'text': 'okay, here all our lists are ready.', 'start': 14799.161, 'duration': 1.961}, {'end': 14802.904, 'text': "now let's create a data frame for it.", 'start': 14801.122, 'duration': 1.782}, {'end': 14809.488, 'text': 'so here df2, that is second data frame, equal pd dot data frame.', 'start': 14802.904, 'duration': 6.584}, {'end': 14811.649, 'text': "so inside this let's define our dictionary.", 'start': 14809.488, 'duration': 2.161}, {'end': 14816.493, 'text': 'say column name is player, which consists of the values from player list.', 'start': 14811.649, 'duration': 4.844}, {'end': 14818.194, 'text': 'next is par.', 'start': 14816.493, 'duration': 1.701}, {'end': 14827.866, 'text': 'so my next column is which consists of the values from par list, and the next is title, again, which consists of the values from title list.', 'start': 14818.194, 'duration': 9.672}, {'end': 14829.487, 'text': "so there's my second data frame.", 'start': 14827.866, 'duration': 1.621}, {'end': 14830.709, 'text': 'let me just print it.', 'start': 14829.487, 'duration': 1.222}, {'end': 14833.232, 'text': 'so here my second data frame is also ready.', 'start': 14830.709, 'duration': 2.523}, {'end': 14836.956, 'text': 'so this one is my first data frame and this one is my second data frame.', 'start': 14833.232, 'duration': 3.724}], 'summary': 'Created two data frames with player, par, and title columns.', 'duration': 37.795, 'max_score': 14799.161, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk14799161.jpg'}, {'end': 15081.369, 'src': 'embed', 'start': 15010.609, 'weight': 6, 'content': [{'end': 15014.53, 'text': 'But in this case all the values from table 1 would be there.', 'start': 15010.609, 'duration': 3.921}, {'end': 15021.972, 'text': 'So our table 1 has, or the data frame 1 has, player 1, player 2 and player 3 right.', 'start': 15015.431, 'duration': 6.541}, {'end': 15024.495, 'text': 'so all these values are over here.', 'start': 15021.972, 'duration': 2.523}, {'end': 15027.439, 'text': 'and apart from it, what else you will get?', 'start': 15024.495, 'duration': 2.944}, {'end': 15033.888, 'text': 'you will get all the values from second data frame and here you will get all the common values from your second data frame.', 'start': 15027.439, 'duration': 6.449}, {'end': 15036.59, 'text': 'so here my player 1 was in both the data frame right.', 'start': 15033.888, 'duration': 2.702}, {'end': 15042.011, 'text': 'so it merged both the table and it showed me these values over here punch and game one right,', 'start': 15036.59, 'duration': 5.421}, {'end': 15046.033, 'text': 'but for player two and player three they were not in the second data frame right.', 'start': 15042.011, 'duration': 4.022}, {'end': 15052.015, 'text': "so that's why panda was not able to identify what value should it give in power and title.", 'start': 15046.033, 'duration': 5.982}, {'end': 15054.876, 'text': 'so that is why it mentioned nan over here.', 'start': 15052.015, 'duration': 2.861}, {'end': 15058.957, 'text': 'okay, so this is how you perform a left merge now.', 'start': 15054.876, 'duration': 4.081}, {'end': 15060.918, 'text': "next let's see how to do a right merge.", 'start': 15058.957, 'duration': 1.961}, {'end': 15064.981, 'text': "So again you don't have to do anything.", 'start': 15063.1, 'duration': 1.881}, {'end': 15067.522, 'text': 'Just change left to right.', 'start': 15065.521, 'duration': 2.001}, {'end': 15070.143, 'text': 'So left becomes right.', 'start': 15068.643, 'duration': 1.5}, {'end': 15071.844, 'text': 'So you got this value.', 'start': 15070.684, 'duration': 1.16}, {'end': 15076.607, 'text': "So why I'm getting this value? So similar to left merge, now it's performing right merge.", 'start': 15072.144, 'duration': 4.463}, {'end': 15079.288, 'text': 'So all the values from your right table would be here.', 'start': 15076.647, 'duration': 2.641}, {'end': 15081.369, 'text': 'Okay, so player 156.', 'start': 15079.688, 'duration': 1.681}], 'summary': 'Demonstration of left and right merge in pandas, with an example of player data from two data frames.', 'duration': 70.76, 'max_score': 15010.609, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk15010609.jpg'}], 'start': 13544.962, 'title': 'Introduction to pandas and data analysis', 'summary': 'Introduces the pandas library for data manipulation, covering its features and advantages over numpy. it also includes creating series objects and data frames, merge operations, join, concatenation, data analysis techniques, and data cleaning methods.', 'chapters': [{'end': 13748.448, 'start': 13544.962, 'title': 'Introduction to pandas library', 'summary': 'Introduces the pandas library, created by wes mckinney in 2015, which is a powerful python library used for data manipulation and analysis, offering features such as handling missing data, data alignment, group by functionality, slicing and subsetting, merging and joining, reshaping, hierarchical labeling of axis, input-output tools, and time series specific functionality.', 'duration': 203.486, 'highlights': ["Handling of missing data Pandas represents missing data as 'nan', making it easy to handle missing data regardless of data type, floating point or non-floating point.", 'Group by functionality Pandas provides a powerful and flexible function for split, apply, and combine operations on data, enabling both aggregating and transforming data.', 'Slicing, indexing, and subsetting Pandas allows intelligent label-based slicing, fancy indexing, and subsetting of large datasets.', 'Merging and joining Pandas facilitates merging and joining operations on datasets.', 'Reshaping Pandas enables easy reshaping and pivoting of data points.']}, {'end': 14389.103, 'start': 13748.448, 'title': 'Introduction to pandas and series object', 'summary': 'Introduces pandas, highlighting its advantages over numpy for data with more than 500,000 rows and its flexibility with label indexing, access to elements, and suitability for tabular, time series, and arbitrary matrix data. it further delves into the series object in pandas, covering its capabilities of holding any data type, creating a series object, checking its type, and changing the index name. finally, it explores the creation of a data frame using lists and dictionaries.', 'duration': 640.655, 'highlights': ['Pandas works better with NumPy for data with more than 500,000 rows, while NumPy is more efficient for 50,000 rows or less. Pandas is preferred over NumPy for data with more than 500,000 rows, indicating its efficiency for large real-world data.', 'Panda series object is more flexible, allowing the definition of custom labels and index to access elements, distinguishing it from numpy arrays accessed by default integer position. The flexibility of Panda series object in defining custom labels and indexing sets it apart from numpy arrays, providing more versatile data access.', 'Pandas is suitable for tabular data, time series data, and arbitrary matrix data, allowing the feeding of these data types into Pandas. Pandas is suitable for tabular, time series, and arbitrary matrix data, expanding its applicability to diverse data structures.', 'The series object in Panda is a one labeled array capable of holding any data type and is more powerful than a list or array in Python. The series object in Panda is a powerful, one-labeled array capable of holding diverse data types, distinguishing it from lists or arrays in Python.', "Creating a series object is possible using the 'pd.series' function, which results in a one-dimensional array labeled with index values. The creation of a series object involves using the 'pd.series' function to generate a one-dimensional array labeled with index values.", "The type of a series object can be checked using the 'type' command, which confirms the object as a Pandas series. The type of a series object can be verified using the 'type' command, enabling the confirmation of its classification as a Pandas series.", "The index name of a series object can be changed using the 'index' parameter, allowing for customized indexing. The 'index' parameter enables the customization of the index name for a series object, providing flexibility in indexing.", 'A data frame in Pandas is a two-dimensional labeled data with columns containing different types of data, and its features include mutability, data access labeling, and arithmetic operations on rows and columns. A data frame in Pandas is a versatile two-dimensional labeled data structure with mutability, data access labeling, and support for arithmetic operations on rows and columns.', "A data frame can be created using the 'pd.dataframe' command, allowing for the construction of a data frame from specified data. A data frame can be generated using the 'pd.dataframe' command, providing a method for constructing a data frame from specified data."]}, {'end': 15133.109, 'start': 14389.103, 'title': 'Data frame creation and merge operations', 'summary': 'Covers the creation of data frames using dictionaries, series, and numpy arrays, and demonstrates merge operations including inner, left, right, and outer merge, with examples and outputs.', 'duration': 744.006, 'highlights': ['Creating data frames using dictionaries, series, and numpy arrays, demonstrating the process and displaying the resulting data frames.', "Performing inner merge on data frames using 'merge' function, explaining the process and output with examples.", 'Explaining left merge and displaying the output with examples, highlighting the inclusion of all values from the left data frame and common values from the right data frame.', 'Demonstrating right merge and displaying the output, emphasizing the inclusion of all values from the right data frame and common values from the left data frame.', "Detailing outer merge, executing the operation, and explaining the result with emphasis on including all values and displaying 'NaN' where values are not common."]}, {'end': 15646.27, 'start': 15133.469, 'title': 'Pandas join and concatenation', 'summary': 'Covers the differences between merge and join operations in pandas, including the use of index values for join, and demonstrates the inner, left, right, and outer join operations. it also explains how to perform concatenation using pandas, and provides an overview of importing and analyzing a dataset, including checking type, viewing records, getting shape, and column summary.', 'duration': 512.801, 'highlights': ['Explaining the difference between merge and join operations in Pandas, emphasizing the use of index values for join instead of attribute names, and the conditions for common attributes in merge operations. None', 'Demonstrating the inner, left, right, and outer join operations in Pandas, including explanations and code examples for each type of join. None', 'Illustrating the process of concatenating data frames in Pandas with code examples. None', 'Providing an overview of importing and analyzing a dataset in Pandas, including checking type, viewing records, getting shape, and column summary. Total memory usage is 3.3 plus kb']}, {'end': 16119.105, 'start': 15646.27, 'title': 'Data analysis and cleaning techniques', 'summary': 'Covers how to calculate mean, median, standard deviation, maximum and minimum values, count, and descriptive statistics summary of a data frame, along with methods for data cleaning such as renaming columns, replacing null values with mean, dropping unwanted columns, finding correlation matrix, and changing data type.', 'duration': 472.835, 'highlights': ['The chapter covers how to calculate mean, median, standard deviation, maximum and minimum values, count, and descriptive statistics summary of a data frame It explains how to calculate the mean, median, standard deviation, maximum and minimum values, count, and descriptive statistics summary of a data frame.', 'Methods for data cleaning such as renaming columns, replacing null values with mean, dropping unwanted columns, finding correlation matrix, and changing data type are explained It details methods for data cleaning including renaming columns, replacing null values with mean, dropping unwanted columns, finding correlation matrix, and changing the data type of attributes.', 'Replacing null values with the mean of the column is demonstrated It demonstrates the process of replacing null values with the mean of the column to improve data analysis.', 'Finding the correlation matrix and interpreting the correlation values between attributes is explained It explains how to find the correlation matrix to interpret the correlation between attributes and make decisions based on the correlation values.', 'Changing the data type of attributes to perform data manipulation is illustrated It illustrates the process of changing the data type of attributes, such as converting from string to float, to enable data manipulation and analysis.']}], 'duration': 2574.143, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk13544962.jpg', 'highlights': ['Pandas is suitable for tabular, time series, and arbitrary matrix data, expanding its applicability to diverse data structures.', "Pandas represents missing data as 'nan', making it easy to handle missing data regardless of data type, floating point or non-floating point.", 'Pandas provides a powerful and flexible function for split, apply, and combine operations on data, enabling both aggregating and transforming data.', 'Pandas allows intelligent label-based slicing, fancy indexing, and subsetting of large datasets.', 'Pandas facilitates merging and joining operations on datasets.', 'Pandas enables easy reshaping and pivoting of data points.', 'Pandas is preferred over NumPy for data with more than 500,000 rows, indicating its efficiency for large real-world data.', 'The flexibility of Panda series object in defining custom labels and indexing sets it apart from numpy arrays, providing more versatile data access.', 'Pandas is a powerful, one-labeled array capable of holding diverse data types, distinguishing it from lists or arrays in Python.', "The creation of a series object involves using the 'pd.series' function to generate a one-dimensional array labeled with index values.", 'A data frame in Pandas is a versatile two-dimensional labeled data structure with mutability, data access labeling, and support for arithmetic operations on rows and columns.', "A data frame can be generated using the 'pd.dataframe' command, providing a method for constructing a data frame from specified data.", 'Creating data frames using dictionaries, series, and numpy arrays, demonstrating the process and displaying the resulting data frames.', "Performing inner merge on data frames using 'merge' function, explaining the process and output with examples.", 'Explaining left merge and displaying the output with examples, highlighting the inclusion of all values from the left data frame and common values from the right data frame.', 'Demonstrating right merge and displaying the output, emphasizing the inclusion of all values from the right data frame and common values from the left data frame.', "Detailing outer merge, executing the operation, and explaining the result with emphasis on including all values and displaying 'NaN' where values are not common.", 'The chapter covers how to calculate mean, median, standard deviation, maximum and minimum values, count, and descriptive statistics summary of a data frame It explains how to calculate the mean, median, standard deviation, maximum and minimum values, count, and descriptive statistics summary of a data frame.', 'Methods for data cleaning such as renaming columns, replacing null values with mean, dropping unwanted columns, finding correlation matrix, and changing data type are explained It details methods for data cleaning including renaming columns, replacing null values with mean, dropping unwanted columns, finding correlation matrix, and changing the data type of attributes.', 'Replacing null values with the mean of the column is demonstrated It demonstrates the process of replacing null values with the mean of the column to improve data analysis.', 'Finding the correlation matrix and interpreting the correlation values between attributes is explained It explains how to find the correlation matrix to interpret the correlation between attributes and make decisions based on the correlation values.', 'Changing the data type of attributes to perform data manipulation is illustrated It illustrates the process of changing the data type of attributes, such as converting from string to float, to enable data manipulation and analysis.']}, {'end': 17300.695, 'segs': [{'end': 17191.341, 'src': 'embed', 'start': 17161.773, 'weight': 0, 'content': [{'end': 17164.775, 'text': 'So there are a lot of data scientists that are available outside.', 'start': 17161.773, 'duration': 3.002}, {'end': 17173.5, 'text': 'One of the data scientists who is working on this particular feature could be a data scientist who is aligned to the PowerPoint team at Microsoft.', 'start': 17165.075, 'duration': 8.425}, {'end': 17178.943, 'text': 'Similarly, you would also have a lot of other products.', 'start': 17174.76, 'duration': 4.183}, {'end': 17181.424, 'text': "We're using, let's say, this particular tool called GoToWebinar.", 'start': 17178.963, 'duration': 2.461}, {'end': 17185.238, 'text': 'And there could be a lot of AI implementations that is happening in the background.', 'start': 17182.277, 'duration': 2.961}, {'end': 17191.341, 'text': 'Like, for example, there could be a team which is working specifically on monitoring the logs of this particular tool.', 'start': 17185.278, 'duration': 6.063}], 'summary': 'Many data scientists work on various microsoft products, like powerpoint, using tools like gotowebinar, with ai implementations and log monitoring.', 'duration': 29.568, 'max_score': 17161.773, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk17161773.jpg'}], 'start': 16119.105, 'title': 'Data science roles and responsibilities', 'summary': 'Provides insights into data science interview nuances, including types of roles, responsibilities, and skill sets, with examples of companies. it also delves into specific responsibilities and skill sets for data science consulting, product development, and research teams, exemplified through a case study.', 'chapters': [{'end': 16460.06, 'start': 16119.105, 'title': 'Data science interview insights', 'summary': 'Discusses the nuances of data science interviews, including types of data science roles, and the differences in responsibilities and skill sets for each role, along with examples of companies in each category.', 'duration': 340.955, 'highlights': ['The chapter discusses the nuances of data science interviews and the differences in responsibilities and skill sets for each role. It provides insights into the types of data science roles, such as consulting, in-house data science teams, and product development companies, and the specific responsibilities and skill sets associated with each role.', 'Different types of companies are explained as examples of each type of data science role. The chapter provides examples such as consulting and services companies like TCS, Infosys, and strategy consulting companies like Bain and McKinsey, as well as in-house data science teams in companies like AT&T, Verizon, Amazon, and Google for product development.', 'The differences in responsibilities and skill sets for each type of data science role are highlighted. It explains how the responsibilities and skill sets differ for data scientists in consulting, in-house data science teams, and product development companies, illustrating the varied nature of data science roles.']}, {'end': 16756.475, 'start': 16460.08, 'title': 'Data science roles and responsibilities', 'summary': "Discusses the roles of data science consulting teams, product development teams, and research teams, emphasizing the specific responsibilities and skill sets required for each role, exemplified through the case of a data science consultant addressing a diamond mining company's equipment failure challenge in africa.", 'duration': 296.395, 'highlights': ['The chapter discusses the roles of data science consulting teams, product development teams, and research teams, emphasizing the specific responsibilities and skill sets required for each role. The chapter provides an overview of the roles and responsibilities of data science consulting teams, product development teams, and research teams, highlighting the specific skill sets and responsibilities for each role.', "The chapter exemplifies the case of a data science consultant addressing a diamond mining company's equipment failure challenge in Africa, emphasizing the need for communication skills and the ability to translate business problems into data science problems. The example illustrates the importance of communication skills for data science consultants, showcasing the need to empathize with clients and translate business problems into data science problems, as demonstrated through the case of addressing equipment failure challenges for a diamond mining company in Africa.", 'The primary skill set across the discussed teams is machine learning and data science, with each role requiring different types of work and ancillary skill sets. The primary skill set across the discussed teams is machine learning and data science, with each role necessitating distinct types of work and additional skill sets.']}, {'end': 17300.695, 'start': 16756.475, 'title': 'Roles in data science', 'summary': 'Discusses the roles of data science consultants, captive data science teams, research teams, and product development teams, highlighting their responsibilities and differences in work pace and variety.', 'duration': 544.22, 'highlights': ['Data science consultants play a critical role in transitioning business problems into data science problems, working on a variety of problems for different clients. Data science consultants are crucial in transitioning business problems into data science problems and work on a variety of problems for different clients.', "Captive data science teams are expected to solve problems in a holistic manner and align themselves with the aims and objectives of the organization, while having more time to work on a single problem. Captive data science teams solve problems in a holistic manner, align with the organization's objectives, and have more time to work on a single problem.", 'Research teams require a lot of academic background and patience, working on slow-paced, cutting-edge research, and requiring a deep skill set. Research teams require academic background, patience, and a deep skill set, working on slow-paced, cutting-edge research.', 'Data scientists in product development teams work on features like automatic layout identification in PowerPoint, AI implementations in various products, and computer vision exercises like background deletion, embedding intelligence into products. Data scientists in product development teams work on automatic layout identification, AI implementations, and computer vision exercises, embedding intelligence into products.']}], 'duration': 1181.59, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk16119105.jpg', 'highlights': ['The chapter provides insights into the types of data science roles, such as consulting, in-house data science teams, and product development companies, and the specific responsibilities and skill sets associated with each role.', "The chapter exemplifies the case of a data science consultant addressing a diamond mining company's equipment failure challenge in Africa, emphasizing the need for communication skills and the ability to translate business problems into data science problems.", 'The primary skill set across the discussed teams is machine learning and data science, with each role requiring different types of work and ancillary skill sets.', 'Data science consultants play a critical role in transitioning business problems into data science problems, working on a variety of problems for different clients.', "Captive data science teams solve problems in a holistic manner, align with the organization's objectives, and have more time to work on a single problem.", 'Research teams require academic background, patience, and a deep skill set, working on slow-paced, cutting-edge research.', 'Data scientists in product development teams work on automatic layout identification, AI implementations, and computer vision exercises, embedding intelligence into products.']}, {'end': 19442.153, 'segs': [{'end': 18165.905, 'src': 'embed', 'start': 18139.532, 'weight': 1, 'content': [{'end': 18146.337, 'text': 'a product sponsor or a product owner, because they want to make sure that you have enough capability to solve that particular problem.', 'start': 18139.532, 'duration': 6.805}, {'end': 18155.965, 'text': 'Again, the depth of interviews may depend from one company to another company, but essentially get ready for at least four to five rounds.', 'start': 18147.638, 'duration': 8.327}, {'end': 18157.539, 'text': 'of interviews.', 'start': 18156.918, 'duration': 0.621}, {'end': 18165.905, 'text': "On an average, I'm talking about on average, in a fairly repeated MNC, get up for four to five different rounds of interviews.", 'start': 18158.559, 'duration': 7.346}], 'summary': 'Product sponsors expect candidates to solve problems, with 4-5 rounds of interviews in mncs.', 'duration': 26.373, 'max_score': 18139.532, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk18139532.jpg'}, {'end': 18276.948, 'src': 'embed', 'start': 18250.912, 'weight': 5, 'content': [{'end': 18255.035, 'text': 'So there was different people who were being called upon, different people who were asking me questions.', 'start': 18250.912, 'duration': 4.123}, {'end': 18262.44, 'text': "But I think it's good in a way because it gets to the flavor of what they want from you and things like that.", 'start': 18256.896, 'duration': 5.544}, {'end': 18266.723, 'text': "So yeah, that's essentially the layout of how your interview generally looks like.", 'start': 18263.181, 'duration': 3.542}, {'end': 18271.423, 'text': 'But I think the more important question is what kind of questions do these guys ask?', 'start': 18267.54, 'duration': 3.883}, {'end': 18275.447, 'text': 'In a technical round, what kind of questions are generally being thrown at?', 'start': 18271.583, 'duration': 3.864}, {'end': 18276.948, 'text': 'you would be thrown at you?', 'start': 18275.447, 'duration': 1.501}], 'summary': 'Interview process involves various people asking questions to gauge candidate suitability and technical skills.', 'duration': 26.036, 'max_score': 18250.912, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk18250912.jpg'}, {'end': 18347.234, 'src': 'embed', 'start': 18321.617, 'weight': 0, 'content': [{'end': 18329.462, 'text': "If you simply come and put your past experience and tell me that I've done a course in data science and I'd like to move into the field of data science,", 'start': 18321.617, 'duration': 7.845}, {'end': 18330.823, 'text': "it's unlikely you will get hired.", 'start': 18329.462, 'duration': 1.361}, {'end': 18334.666, 'text': "It's unlikely that you will find people who will be interested in you.", 'start': 18330.863, 'duration': 3.803}, {'end': 18338.909, 'text': 'You have to find some common ground between yourself and the interviewer.', 'start': 18335.286, 'duration': 3.623}, {'end': 18342.831, 'text': 'The way you could do that is by putting some kind of data science experience in place.', 'start': 18338.949, 'duration': 3.882}, {'end': 18347.234, 'text': "What kind of experience do you put in? Let's say you've solved a Kaggle problem.", 'start': 18343.812, 'duration': 3.422}], 'summary': 'Past data science experience crucial for getting hired, e.g. solving a kaggle problem.', 'duration': 25.617, 'max_score': 18321.617, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk18321617.jpg'}, {'end': 18484.464, 'src': 'embed', 'start': 18456.584, 'weight': 4, 'content': [{'end': 18460.766, 'text': 'And Kaggle problems, people do recognize that as valid experience.', 'start': 18456.584, 'duration': 4.182}, {'end': 18463.839, 'text': "So let's say you solve a particular Kaggle problem.", 'start': 18462.159, 'duration': 1.68}, {'end': 18465.96, 'text': 'What kind of questions can you expect from those??', 'start': 18464.119, 'duration': 1.841}, {'end': 18471.301, 'text': 'You will be asked about what kind of steps did you perform? What kind of?', 'start': 18466.5, 'duration': 4.801}, {'end': 18472.881, 'text': "let's say, what was the problem statement about?", 'start': 18471.301, 'duration': 1.58}, {'end': 18474.482, 'text': 'Why are you solving that particular problem?', 'start': 18472.921, 'duration': 1.561}, {'end': 18477.162, 'text': 'What is the business impact of solving that particular problem?', 'start': 18474.922, 'duration': 2.24}, {'end': 18484.464, 'text': 'And you will have questions about what kind of pre-processing did you do?', 'start': 18477.822, 'duration': 6.642}], 'summary': 'Kaggle experience valued, expect questions on problem-solving steps, business impact, and pre-processing.', 'duration': 27.88, 'max_score': 18456.584, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk18456584.jpg'}], 'start': 17300.695, 'title': 'Data science interviews', 'summary': 'Discusses data science roles, interview stages, process, and tips, including distinctions between roles, interview stages, skill sets, evaluation methods, coding and sql skills, interview rounds, face-to-face preference, question types, customer churn prediction in telecom, business impact, and data preparation.', 'chapters': [{'end': 17412.168, 'start': 17300.695, 'title': 'Data science roles and responsibilities', 'summary': 'Discusses the experiences of a data scientist in a product team, highlighting the satisfaction of seeing built features go out successfully, but also the potential drawback of work becoming repetitive over time. it also clarifies the distinctions between data scientist, data analyst, and business analyst roles.', 'duration': 111.473, 'highlights': ['The best part of being in a product team is seeing the features you build go out successfully.', 'The potential drawback of working as a data scientist is the repetitive nature of the work over time.', 'Clarification on the distinctions between data scientist, data analyst, and business analyst roles.']}, {'end': 17801.062, 'start': 17413.496, 'title': 'Data science interview stages', 'summary': 'Discusses the three stages of a data science interview, including resume filtering, hands-on sessions, and types of assignments, with emphasis on the required skill sets and evaluation methods.', 'duration': 387.566, 'highlights': ['The importance of skill sets in filtering resumes, particularly in Python, R, SQL, big data, and deep learning frameworks, as a qualification benchmark. The transcript emphasizes the significance of having specific skill sets such as Python, R, SQL, big data, and deep learning frameworks like TensorFlow or PyTorch as a means to qualify for the initial screening of resumes.', 'The value of referrals in the shortlisting process, with emphasis on personal connections and the challenges of evaluating data scientists based solely on paper qualifications. It is mentioned that data science roles are often filled through referrals due to the difficulty of evaluating candidates solely based on paper qualifications, highlighting the importance of personal connections in the shortlisting process.', 'The use of hands-on sessions and online platforms like Codality and Hacker Earth to evaluate practical skills, with specific mention of the types of challenges and their duration. The chapter discusses the use of hands-on sessions and online platforms like Codality and Hacker Earth to assess practical skills, including the types of challenges such as on-site and take-home assignments, with varying durations from hours to weeks.']}, {'end': 18295.577, 'start': 17801.062, 'title': 'Data science interview process', 'summary': 'Discusses the importance of hands-on sessions in data science interviews, emphasizing the need for strong coding and sql skills, followed by multiple technical rounds and non-technical rounds, with an average of four to five rounds of interviews, and a preference for face-to-face interviews.', 'duration': 494.515, 'highlights': ['Hands-on sessions are crucial in data science interviews, especially for freshers, as they test coding skills and may include SQL-based questions. Companies conduct hands-on sessions to evaluate candidates, including freshers, for their coding and SQL skills, with the emphasis on showcasing a wide variety of problem-solving skills. Freshers are hired based on their coding skills, and SQL-based questions are also expected during hands-on sessions.', "Technical interviews typically consist of two to three rounds, with data scientists and managers asking hardcore technical questions. Technical interviews involve two to three rounds, with data scientists and managers asking hardcore technical questions to evaluate candidates' skills and knowledge in data science.", "Non-technical rounds are led by business-oriented personnel, evaluating candidates on their business acumen and ability to translate business problems into data science problems. Non-technical rounds are conducted by business-oriented personnel, such as product owners or delivery heads, to assess candidates' understanding of businesses and their capability to solve business problems using data science.", 'The interview process typically involves four to five rounds, with a preference for face-to-face interviews, and companies may conduct preliminary telephonic rounds before face-to-face interviews. On average, candidates can expect four to five rounds of interviews, with a preference for face-to-face interviews, although preliminary telephonic rounds may be conducted initially.']}, {'end': 19041.678, 'start': 18296.037, 'title': 'Data science interview tips', 'summary': 'Discusses the types of questions asked in data science interviews, emphasizing the importance of previous experience and role-based knowledge, as well as the significance of clear communication skills.', 'duration': 745.641, 'highlights': ["Data science interviews focus on previous experience and role-based knowledge. Interview questions are primarily based on the candidate's previous experience and the role they are interviewing for, emphasizing the importance of relevant work and role-based knowledge in the field of data science.", "Emphasis on communication skills in explaining complex algorithms to non-technical individuals. The interview process includes assessing the candidate's ability to communicate complex data science concepts in a simple manner to individuals who lack technical understanding, highlighting the significance of effective communication skills in data science roles.", 'Encouragement to be honest about knowledge and experience. Candidates are encouraged to be transparent about their knowledge and experience, with the advice to confidently acknowledge when they lack specific expertise, rather than attempting to overinflate their qualifications.']}, {'end': 19442.153, 'start': 19041.678, 'title': 'Predicting customer churn in telecom', 'summary': 'Discusses the importance of predicting customer churn in telecom companies, the reasons behind it, and the factors considered in preparing the data for churn prediction, with a focus on business impact and available data.', 'duration': 400.475, 'highlights': ['The importance of predicting churn is to identify customers likely to quit and intervene to retain them, with examples of possible interventions such as offering discounts or free services. ', 'Predicting churn is important for a company like AT&T, where the churn rate is around 6%, and reducing the number of customers quitting is a primary objective. Churn rate: 6%', 'Factors considered in preparing the data for churn prediction include billing reasons, customer care information, and demography, with age and location being significant variables. ']}], 'duration': 2141.458, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk17300695.jpg', 'highlights': ['The interview process typically involves four to five rounds, with a preference for face-to-face interviews, and companies may conduct preliminary telephonic rounds before face-to-face interviews.', 'The importance of skill sets in filtering resumes, particularly in Python, R, SQL, big data, and deep learning frameworks, as a qualification benchmark.', 'The use of hands-on sessions and online platforms like Codality and Hacker Earth to evaluate practical skills, with specific mention of the types of challenges and their duration.', 'The value of referrals in the shortlisting process, with emphasis on personal connections and the challenges of evaluating data scientists based solely on paper qualifications.', 'The potential drawback of working as a data scientist is the repetitive nature of the work over time.', 'The importance of predicting churn is to identify customers likely to quit and intervene to retain them, with examples of possible interventions such as offering discounts or free services.']}, {'end': 22628.428, 'segs': [{'end': 21855.984, 'src': 'embed', 'start': 21816.312, 'weight': 6, 'content': [{'end': 21819.273, 'text': 'then it might be slightly tricky for you to navigate yourself through it.', 'start': 21816.312, 'duration': 2.961}, {'end': 21820.873, 'text': 'So just look for enough guidance.', 'start': 21819.313, 'duration': 1.56}, {'end': 21823.333, 'text': 'So if you have somebody to guide you through the whole process.', 'start': 21820.893, 'duration': 2.44}, {'end': 21826.954, 'text': "So if it's a slightly larger team,", 'start': 21824.114, 'duration': 2.84}, {'end': 21836.371, 'text': "then you'll have enough time to prepare yourself before you launch or you'll have enough time to ramp up before you actually, uh,", 'start': 21826.954, 'duration': 9.417}, {'end': 21844.877, 'text': "get completely onboarded onto the uh, uh, you, you where, or you'll have enough people who would have solved problems already.", 'start': 21836.371, 'duration': 8.506}, {'end': 21848.239, 'text': "so they'll help you guide, guide yourself through and navigate yourself through.", 'start': 21844.877, 'duration': 3.362}, {'end': 21851.461, 'text': "secondly, what it tells you is, if it's an existing team, then things are slightly more streamlined.", 'start': 21848.239, 'duration': 3.222}, {'end': 21855.984, 'text': "you're not going to give be given random work for data entry and things like that.", 'start': 21851.461, 'duration': 4.523}], 'summary': 'Having guidance and an existing team streamlines the onboarding process.', 'duration': 39.672, 'max_score': 21816.312, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk21816312.jpg'}, {'end': 22253.535, 'src': 'embed', 'start': 22219.8, 'weight': 3, 'content': [{'end': 22223.483, 'text': 'Know your stuff before you jump into it.', 'start': 22219.8, 'duration': 3.683}, {'end': 22232.512, 'text': 'The most important thing, again, is around designations.', 'start': 22226.406, 'duration': 6.106}, {'end': 22236.115, 'text': 'So this was somebody who was asking me this point earlier.', 'start': 22233.993, 'duration': 2.122}, {'end': 22241.168, 'text': 'you would often find different designations on job portals.', 'start': 22238.467, 'duration': 2.701}, {'end': 22250.793, 'text': 'you would find data analyst, business analyst, data engineer, data platform engineer, big data engineer, data architect, big data architect,', 'start': 22241.168, 'duration': 9.625}, {'end': 22253.535, 'text': 'and so on and so forth.', 'start': 22250.793, 'duration': 2.742}], 'summary': 'Understanding various job designations is crucial for entering the field of data analysis and engineering.', 'duration': 33.735, 'max_score': 22219.8, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk22219800.jpg'}, {'end': 22306.942, 'src': 'embed', 'start': 22281.772, 'weight': 2, 'content': [{'end': 22291.214, 'text': 'the name data analyst is generally used to people who do data entry, who basically look at data and fill data data entry in certain companies.', 'start': 22281.772, 'duration': 9.442}, {'end': 22297.355, 'text': 'the word data analyst is also about someone who does some kind of dashboarding,', 'start': 22291.214, 'duration': 6.141}, {'end': 22306.942, 'text': 'looks at dashboards and who performs some SQL querying and does some kind of data dashboarding and builds reports and things like that.', 'start': 22297.355, 'duration': 9.587}], 'summary': 'Data analysts perform data entry, dashboarding, and sql querying.', 'duration': 25.17, 'max_score': 22281.772, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk22281772.jpg'}, {'end': 22552.337, 'src': 'embed', 'start': 22510.205, 'weight': 0, 'content': [{'end': 22512.667, 'text': 'Data science lead, data science team manager, and things like that.', 'start': 22510.205, 'duration': 2.462}, {'end': 22517.449, 'text': "So for freshers, you're looking at data scientists, junior data science roles.", 'start': 22512.927, 'duration': 4.522}, {'end': 22523.531, 'text': "and for some people who are slightly experienced, you're looking at data science or senior data science roles.", 'start': 22518.909, 'duration': 4.622}, {'end': 22530.314, 'text': 'i would not suggest you to get into a senior data science role because senior data science role expects you to be,', 'start': 22523.531, 'duration': 6.783}, {'end': 22533.775, 'text': 'expect you to have prior experience in the field of data science.', 'start': 22530.314, 'duration': 3.461}, {'end': 22538.657, 'text': 'so senior data science role is a very, is a very tricky role.', 'start': 22533.775, 'duration': 4.882}, {'end': 22545.46, 'text': 'i mean i know most of you might want to, might already have had experience in other i.t sectors,', 'start': 22538.657, 'duration': 6.803}, {'end': 22552.337, 'text': "but it's not probably directly possible to move into a senior data science role or a team lead data science.", 'start': 22546.355, 'duration': 5.982}], 'summary': 'Entry-level roles for freshers, senior roles require prior experience in data science.', 'duration': 42.132, 'max_score': 22510.205, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk22510205.jpg'}], 'start': 19442.153, 'title': 'Predicting churn factors and data science interviews', 'summary': 'Discusses critical factors in predicting customer churn and emphasizes effective data science interview preparation, with insights into interview insights and hiring algorithm for facebook data science team.', 'chapters': [{'end': 19511.401, 'start': 19442.153, 'title': 'Predicting churn factors', 'summary': 'Discusses the critical factors in predicting customer churn, highlighting the importance of demography and the impact of value-added services on customer loyalty.', 'duration': 69.248, 'highlights': ['Demography is a critical factor in predicting customer churn, with a higher likelihood of younger customers quitting compared to older ones.', 'The usage of value-added services, such as voicemail and missed call alerts, plays a significant role in predicting customer loyalty and churn rates.']}, {'end': 19981.534, 'start': 19511.401, 'title': 'Data preparation and analysis', 'summary': 'Covers the process of data preparation, including outlier removal, missing value treatment, and model building, with emphasis on exploratory analysis and identifying key variables for predictive modeling.', 'duration': 470.133, 'highlights': ['Explaining data preparation techniques: The speaker discusses data preprocessing, outlier removal, feature engineering, and splitting the data into train and test sets, with an emphasis on handling missing values.', 'Discussions on outlier identification and handling: The speaker explains the concept of outliers, methods for identifying them such as Boxplot and z-score, and the decision-making process for handling outliers based on data size and significance.', 'Exploratory analysis findings: The speaker describes the findings from univariate and multivariate analysis, including the negative correlation between age and churn rate, age and bill amount, and the impact of location on churn rate.']}, {'end': 20338.588, 'start': 19983.055, 'title': 'Effective data science interview preparation', 'summary': 'Emphasizes the importance of understanding and articulating exploratory analysis, model building, evaluation metrics, and the business impact of data science projects in interviews, suggesting that candidates should be well-prepared to answer questions related to these areas.', 'duration': 355.533, 'highlights': ['Candidates should be able to clearly explain their experimentation process and articulate the reasons behind their choice of models, such as logistic regression or random forest, and demonstrate their understanding of model evaluation through metrics like confusion matrix, precision, recall, and F1 score.', "It is crucial for candidates to have a clear understanding of evaluation metrics for both classification and regression problems, including precision, recall, and F1 score for classification, and MAE, MSE, RMSE, and MAPE for regression, as these are key indicators of a candidate's proficiency in data science.", 'Candidates are advised to prepare for interview questions by ensuring they can articulate the business impact of their data science projects, especially for open source problems like Kaggle, and to showcase their work by sharing project code and notebooks on platforms like GitHub to enhance credibility and visibility.']}, {'end': 20784.571, 'start': 20338.588, 'title': 'Data science interview insights', 'summary': 'Emphasizes the importance of focusing on supervised and unsupervised learning for data science interviews, with approximately 60-70% of problems being supervised, 30-35% unsupervised, and 2-3% reinforcement learning. it also discusses explaining data science concepts like linear regression and offers insights into handling case study-based questions during interviews.', 'duration': 445.983, 'highlights': ['The majority of problems in data science interviews are primarily supervised learning problems, comprising approximately 60-70% of the total problems. This is followed by unsupervised learning problems, which make up around 30-35% of the problems.', 'Reinforcement learning problems account for only 2-3% of the total problems in data science interviews, indicating the lower emphasis on this technique compared to supervised and unsupervised learning.', 'The explanation of data science concepts, such as linear regression, involves interpreting the impact of variables on the target, like how a unit increase in ad revenue positively impacts sales by $10 and how an increase in the number of competitors leads to a decrease in sales by $5, providing a practical approach to explaining complex concepts.', 'Case study-based questions in interviews involve solving specific scenarios provided by the interviewer, highlighting the importance of being prepared to handle such challenges during the interview process.']}, {'end': 21531.17, 'start': 20784.731, 'title': 'Hiring algorithm for facebook data science team', 'summary': 'Discusses the challenge of hiring the most suitable candidate out of 10,000 applicants for a data science role at facebook, using data science techniques to identify the best candidates based on similarity to the existing team members and explores the approach of solving the problem as both a supervised and unsupervised learning problem.', 'duration': 746.439, 'highlights': ['The challenge of hiring the most suitable candidate out of 10,000 applicants for a data science role at Facebook is discussed, emphasizing the need to identify the best candidates based on similarity to the existing team members and the approach of solving the problem as both a supervised and unsupervised learning problem.', 'The need to build an algorithm to automatically identify the best person or the most eligible people out of the 10,000 applicants for the data science role at Facebook is highlighted, stressing the impossibility of interviewing all 10,000 applicants and the responsibility to devise an algorithm to automatically shrink the size of people to be interviewed.', "The discussion of utilizing data science techniques to solve the hiring problem for Facebook's data science team, focusing on filtering candidates based on experience, skill sets, likes, dislikes, location, and other information to ensure a perfect cultural fit within the team is emphasized.", 'The exploration of solving the hiring problem as both a supervised and unsupervised learning problem is highlighted, explaining the need for historical information about who was previously interviewed and if they got hired or rejected to solve it as a classification problem and the approach of solving it as an unsupervised learning problem by identifying the best candidates without historical information.']}, {'end': 22628.428, 'start': 21531.17, 'title': 'Unsupervised learning for data science', 'summary': 'Discusses the utilization of unsupervised learning for data science, including methods such as computing mean observations, performing clustering, and interviewing groups closest to existing data points, as well as the importance of asking relevant questions during a data science interview.', 'duration': 1097.258, 'highlights': ['The process involves computing mean observations for a given dataset and performing clustering on a larger dataset of 10,000 people. Performing unsupervised learning through computing mean observations and clustering on large datasets.', 'Identifying the group closest to the existing data points through methods like nearest neighbor compute distance and selecting that group for further interviewing. Selecting the group closest to existing data points for further interviewing through nearest neighbor compute distance.', "The importance of asking relevant questions during a data science interview, particularly about the team, manager's understanding of data science, and availability of data. Emphasizing the criticality of asking relevant questions during a data science interview, including team dynamics, manager's understanding of data science, and data availability."]}], 'duration': 3186.275, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk19442153.jpg', 'highlights': ['The usage of value-added services plays a significant role in predicting customer loyalty and churn rates.', 'Demography is a critical factor in predicting customer churn, with a higher likelihood of younger customers quitting compared to older ones.', 'The majority of problems in data science interviews are primarily supervised learning problems, comprising approximately 60-70% of the total problems.', 'Explaining the need for historical information about who was previously interviewed and if they got hired or rejected to solve the hiring problem as a classification problem.', 'The need to build an algorithm to automatically identify the best person or the most eligible people out of the 10,000 applicants for the data science role at Facebook is highlighted.', 'Candidates are advised to prepare for interview questions by ensuring they can articulate the business impact of their data science projects and to showcase their work by sharing project code and notebooks on platforms like GitHub to enhance credibility and visibility.', 'The process involves computing mean observations for a given dataset and performing clustering on a larger dataset of 10,000 people.', "The importance of asking relevant questions during a data science interview, particularly about the team, manager's understanding of data science, and availability of data.", 'Explaining data preparation techniques: The speaker discusses data preprocessing, outlier removal, feature engineering, and splitting the data into train and test sets, with an emphasis on handling missing values.', "The discussion of utilizing data science techniques to solve the hiring problem for Facebook's data science team, focusing on filtering candidates based on experience, skill sets, likes, dislikes, location, and other information to ensure a perfect cultural fit within the team is emphasized."]}, {'end': 24334.704, 'segs': [{'end': 23415.494, 'src': 'embed', 'start': 23387.254, 'weight': 3, 'content': [{'end': 23390.775, 'text': "Google doesn't know what exactly is running in my mind.", 'start': 23387.254, 'duration': 3.521}, {'end': 23401.569, 'text': 'When I say I want to read a book, Google would, of course give me a a suggestion in the sense that what is it that most users find interesting?', 'start': 23390.795, 'duration': 10.774}, {'end': 23407.851, 'text': "right, but they do it doesn't know that whether i would definitely like that piece, that like that book or not, right?", 'start': 23401.569, 'duration': 6.282}, {'end': 23415.494, 'text': "so what could happen is i might waste a lot of time, let's say, browsing around the internet and and maybe crawling across different websites, right,", 'start': 23407.851, 'duration': 7.643}], 'summary': "Frustration with google's inability to accurately suggest books, leading to potential time wasted browsing online.", 'duration': 28.24, 'max_score': 23387.254, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk23387254.jpg'}, {'end': 23662.716, 'src': 'embed', 'start': 23634.145, 'weight': 0, 'content': [{'end': 23636.087, 'text': 'and then present you options.', 'start': 23634.145, 'duration': 1.942}, {'end': 23637.828, 'text': 'the same thing is happening here as well, right?', 'start': 23636.087, 'duration': 1.741}, {'end': 23647.135, 'text': "i mean instead of you telling, uh, the the product right, let's say youtube that what you want to watch or what you come to watch,", 'start': 23637.828, 'duration': 9.307}, {'end': 23648.216, 'text': 'it preempts that right.', 'start': 23647.135, 'duration': 1.081}, {'end': 23653.04, 'text': "of course you don't need to do that, because youtube has all your data, right.", 'start': 23648.216, 'duration': 4.824}, {'end': 23659.595, 'text': 'all the platforms have all your data, so they know what you want to watch or what you want to do right,', 'start': 23653.04, 'duration': 6.555}, {'end': 23662.716, 'text': 'and based on that they generate recommendations for you.', 'start': 23659.595, 'duration': 3.121}], 'summary': 'Platforms use data to preempt and generate recommendations for users.', 'duration': 28.571, 'max_score': 23634.145, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk23634145.jpg'}], 'start': 22629.028, 'title': 'Recommendation engines and algorithms', 'summary': 'Explores recommendation engines, their personalized nature, and impact on platforms like youtube, netflix, and amazon prime, emphasizing their role in improving customer experience, increasing user engagement, and influencing consumer behavior through superior data packages and algorithms, including collaborative filtering.', 'chapters': [{'end': 22931.123, 'start': 22629.028, 'title': 'Understanding recommendation engines', 'summary': 'Explores how recommendation engines analyze user behavior to generate personalized content recommendations, leading to increased user engagement and ad revenue for platforms like youtube.', 'duration': 302.095, 'highlights': ['Platforms like YouTube, Amazon Prime, Z5, and Netflix use recommendation engines to suggest personalized content based on user behavior, leading to increased user engagement and ad revenue.', 'Recommendation engines analyze user behavior, such as the type of content consumed, preferred genres, language preferences, and duration of videos, to build a user profile and generate personalized content recommendations.', 'By preemptively suggesting content that aligns with user preferences, recommendation engines aim to increase user engagement, resulting in more time spent on the platform and higher opportunities for ad revenue.']}, {'end': 23252.078, 'start': 22931.123, 'title': 'Understanding recommendation engines', 'summary': 'Discusses recommendation engines, their prevalence across platforms like youtube, netflix, amazon prime, and social media, the personalized nature of recommendations, and their application in predicting and influencing consumer behavior.', 'duration': 320.955, 'highlights': ['The prevalence of recommendation engines across platforms like YouTube, Netflix, Amazon Prime, Facebook, LinkedIn, and more, and their role in suggesting content based on user behavior and consumption patterns.', 'The personalized and user-specific nature of recommendations, which are tailored to individual consumption patterns and preferences to enhance user experience and engagement.', 'The predictive nature of recommendation engines, which analyze past user behavior to anticipate and suggest the next likely content or product that the user may be interested in, thereby influencing consumer behavior and purchase decisions.', "The concept of 'propensity to buy' or 'next best purchase' algorithms as another way of utilizing data to predict and influence consumer behavior, particularly in upselling and improving sales for businesses."]}, {'end': 23586.26, 'start': 23252.078, 'title': 'Understanding recommendation engines', 'summary': 'Discusses the impact of superior data packages on consumer spending, the importance of recommendation engines in improving customer experience and increasing user engagement, and the role of algorithms and data in making relevant item recommendations.', 'duration': 334.182, 'highlights': ['Superior data packages lead to increased spending by consumers, as they end up paying more money over time compared to using inferior services. Switching to a superior data package results in fixed monthly costs, leading to higher overall expenditure compared to using inferior services.', 'Recommendation engines, such as Goodreads, significantly improve customer experience by reducing the time spent on searching for books and content and providing relevant suggestions based on past behavior. Goodreads, as an example of a recommendation engine, recommends books based on past reading and rating behavior, leading to a more efficient and enjoyable customer experience.', 'Recommendation engines not only benefit customers by providing a better experience but also ensure that organizations increase user engagement and profitability through continued user interaction and content consumption. Recommendation engines create a win-win situation by enhancing customer experience and increasing the amount of time users spend on the platform, thereby benefiting both customers and organizations.']}, {'end': 24075.034, 'start': 23586.26, 'title': 'Recommendation engines overview', 'summary': 'Explains recommendation engines, including the types of recommendation engines, such as collaborative filtering, content-based filtering, and hybrid recommendation engines, and how they generate recommendations based on user preferences and content similarity.', 'duration': 488.774, 'highlights': ['The chapter explains the three types of recommendation engines: collaborative filtering, content-based filtering, and hybrid recommendation engines. The explanation provides an overview of the different types of recommendation engines, laying the groundwork for the subsequent details.', "Collaborative filtering generates recommendations based on the similarity between user preferences, looking at consumption and preferences to find lookalike users and recommend content based on their preferences. This highlight delves into the process of collaborative filtering, detailing how it analyzes user preferences to generate recommendations based on similar users' preferences.", 'Content-based filtering recommends products or content similar to what the user has liked or watched before, focusing on content similarity rather than user preferences. This point explains how content-based filtering operates by recommending content based on the similarity to previously liked or watched content, without relying on user preferences.', 'Hybrid recommendation engines are a combination of collaborative filtering and content-based filtering, utilizing both methods to generate recommendations. The explanation highlights the combination of collaborative filtering and content-based filtering in hybrid recommendation engines, showcasing the integration of both methods for recommendation generation.']}, {'end': 24334.704, 'start': 24075.034, 'title': 'Collaborative filtering algorithms', 'summary': 'Discusses collaborative filtering algorithms, focusing on user-based and item-based collaborative filtering, with examples and explanations of how recommendations are made based on similar user preferences and consumption behavior.', 'duration': 259.67, 'highlights': ['User-based collaborative filtering identifies similar users based on preferences to recommend new items, while item-based collaborative filtering identifies similarities between items to make recommendations.', 'In user-based collaborative filtering, recommendations are made to an active user based on the rating given by similar users on items not rated by the active user.', 'User A has liked and watched three movies - Interstellar, Inception, and Predestination, while User B has also seen Inception and Predestination, showcasing how collaborative filtering algorithms can identify similar users based on their preferences and make recommendations accordingly.']}], 'duration': 1705.676, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk22629028.jpg', 'highlights': ['Platforms like YouTube, Amazon Prime, Z5, and Netflix use recommendation engines to suggest personalized content based on user behavior, leading to increased user engagement and ad revenue.', 'Recommendation engines analyze user behavior, such as the type of content consumed, preferred genres, language preferences, and duration of videos, to build a user profile and generate personalized content recommendations.', 'The prevalence of recommendation engines across platforms like YouTube, Netflix, Amazon Prime, Facebook, LinkedIn, and more, and their role in suggesting content based on user behavior and consumption patterns.', 'The personalized and user-specific nature of recommendations, which are tailored to individual consumption patterns and preferences to enhance user experience and engagement.', 'The predictive nature of recommendation engines, which analyze past user behavior to anticipate and suggest the next likely content or product that the user may be interested in, thereby influencing consumer behavior and purchase decisions.', 'Superior data packages lead to increased spending by consumers, as they end up paying more money over time compared to using inferior services. Switching to a superior data package results in fixed monthly costs, leading to higher overall expenditure compared to using inferior services.', 'Recommendation engines, such as Goodreads, significantly improve customer experience by reducing the time spent on searching for books and content and providing relevant suggestions based on past behavior.', 'The chapter explains the three types of recommendation engines: collaborative filtering, content-based filtering, and hybrid recommendation engines.', 'User-based collaborative filtering identifies similar users based on preferences to recommend new items, while item-based collaborative filtering identifies similarities between items to make recommendations.']}, {'end': 26232.206, 'segs': [{'end': 25214.836, 'src': 'embed', 'start': 25186.615, 'weight': 6, 'content': [{'end': 25190.256, 'text': "For that, I think I'm going to show you a very simple example.", 'start': 25186.615, 'duration': 3.641}, {'end': 25201.359, 'text': "And by the way, this also will give you an idea in terms of how the data gets structured when you're building a recommendation engine.", 'start': 25191.476, 'duration': 9.883}, {'end': 25205.501, 'text': 'This is how you would have to structure your data as well.', 'start': 25201.399, 'duration': 4.102}, {'end': 25210.042, 'text': "When you're building a recommendation engine, you will have to build a matrix, a matrix sort of a structure.", 'start': 25205.541, 'duration': 4.501}, {'end': 25214.836, 'text': 'okay, a matrix sort of a structure.', 'start': 25211.854, 'duration': 2.982}], 'summary': 'Building a recommendation engine requires structuring data into a matrix.', 'duration': 28.221, 'max_score': 25186.615, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk25186615.jpg'}, {'end': 25554.376, 'src': 'embed', 'start': 25528.025, 'weight': 0, 'content': [{'end': 25535.483, 'text': 'so in this case, user a and user c have movie number two and movie number four common that they have.', 'start': 25528.025, 'duration': 7.458}, {'end': 25538.906, 'text': 'they watched two and they watched four, both of them right.', 'start': 25535.483, 'duration': 3.423}, {'end': 25540.507, 'text': 'so what you would do is you would.', 'start': 25538.906, 'duration': 1.601}, {'end': 25541.367, 'text': 'this is how you will calculate.', 'start': 25540.507, 'duration': 0.86}, {'end': 25550.033, 'text': 'okay. so you will do the rating that a has given to movie two minus the mean user rating okay, one minus three,', 'start': 25541.367, 'duration': 8.666}, {'end': 25554.376, 'text': 'multiplied by the rating that user c has given to the movie.', 'start': 25550.033, 'duration': 4.343}], 'summary': 'Calculating similarity between users based on movie ratings and mean user rating.', 'duration': 26.351, 'max_score': 25528.025, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk25528025.jpg'}, {'end': 26054.587, 'src': 'embed', 'start': 26028.714, 'weight': 12, 'content': [{'end': 26033.196, 'text': 'So yeah, data cleaning is something which you would do essentially before the step right?', 'start': 26028.714, 'duration': 4.482}, {'end': 26038.859, 'text': 'But in this step, whatever is qualified will basically be considered in the ratings.', 'start': 26033.216, 'duration': 5.643}, {'end': 26042.263, 'text': "All right, so that's a recommendation.", 'start': 26040.362, 'duration': 1.901}, {'end': 26044.263, 'text': "That's the user-based collaborative filtering right?", 'start': 26042.303, 'duration': 1.96}, {'end': 26048.945, 'text': 'Item-based collaborative filtering works very similar to user-based collaborative filtering right?', 'start': 26044.424, 'duration': 4.521}, {'end': 26054.587, 'text': "So if you understand how user-based collaborative filtering is, it's the same formula that is basically getting applied here.", 'start': 26048.965, 'duration': 5.622}], 'summary': 'Data cleaning is done before rating items; user-based and item-based collaborative filtering use similar formula.', 'duration': 25.873, 'max_score': 26028.714, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk26028714.jpg'}, {'end': 26148.68, 'src': 'embed', 'start': 26118.261, 'weight': 5, 'content': [{'end': 26121.584, 'text': 'Someone who buys Orange also buys Apple in the same transaction.', 'start': 26118.261, 'duration': 3.323}, {'end': 26125.609, 'text': 'Someone who buys Apple also tends to buy Orange in the same transaction.', 'start': 26121.604, 'duration': 4.005}, {'end': 26128.813, 'text': 'And that is being seen in 67% of the data.', 'start': 26125.669, 'duration': 3.144}, {'end': 26130.755, 'text': 'Assume there are only three customers in your database.', 'start': 26128.853, 'duration': 1.902}, {'end': 26134.467, 'text': "right. and of course it doesn't mean that you would have three customers.", 'start': 26131.704, 'duration': 2.763}, {'end': 26135.087, 'text': 'you would have all.', 'start': 26134.467, 'duration': 0.62}, {'end': 26136.629, 'text': 'you would have millions of customers, right?', 'start': 26135.087, 'duration': 1.542}, {'end': 26139.271, 'text': 'you extrapolate the same percentages in your in your data set right.', 'start': 26136.629, 'duration': 2.642}, {'end': 26148.68, 'text': "so let's say, if this is observed in 60 of 67 of the cases, two i, two out of three cases every three cases, which is in every two out of three cases,", 'start': 26139.271, 'duration': 9.409}], 'summary': '67% of transactions show customers buying both orange and apple together.', 'duration': 30.419, 'max_score': 26118.261, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk26118261.jpg'}, {'end': 26203.59, 'src': 'embed', 'start': 26177.27, 'weight': 7, 'content': [{'end': 26182.894, 'text': 'because they bought a product which is frequently bought with orange.', 'start': 26177.27, 'duration': 5.624}, {'end': 26183.494, 'text': "That's how you are.", 'start': 26182.934, 'duration': 0.56}, {'end': 26190.043, 'text': "item-based collaborative filtering works and it's exactly the same as association rule mining, isn't it right?", 'start': 26184.46, 'duration': 5.583}, {'end': 26191.644, 'text': 'you can use the concept of support,', 'start': 26190.043, 'duration': 1.601}, {'end': 26197.967, 'text': 'confidence and and lift to identify what are those products that get a very high lift right in their purchase when they combine,', 'start': 26191.644, 'duration': 6.323}, {'end': 26203.59, 'text': 'when they combine with other products, right, and anything which has a lift greater than one can be shown as determination to users.', 'start': 26197.967, 'duration': 5.623}], 'summary': 'Item-based collaborative filtering uses support, confidence, and lift to identify products with high lift in purchase combinations.', 'duration': 26.32, 'max_score': 26177.27, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk26177270.jpg'}], 'start': 24334.704, 'title': 'Collaborative filtering in movie recommendations', 'summary': 'Discusses collaborative filtering in movie recommendations, covering user-based and item-based approaches, frequently bought products, user preferences, and item co-occurrence, with potential impacts on recommendation accuracy and user engagement, as well as the process of generating movie recommendations using a recommendation engine.', 'chapters': [{'end': 24500.349, 'start': 24334.704, 'title': 'Collaborative filtering in movie recommendations', 'summary': 'Discusses collaborative filtering in movie recommendations, highlighting how user-based and item-based approaches identify similar users or items to make personalized recommendations, leveraging user preferences and behavior signals.', 'duration': 165.645, 'highlights': ["User-based collaborative filtering identifies similar users and recommends items based on their preferences and behavior signals. It establishes similarity between users' preferences and recommends items that similar users have watched and liked.", 'Item-based collaborative filtering calculates item similarity and recommends top similar items based on user preferences. It involves finding top similar items to non-rated items based on item preferences, similar to association rule mining.', "Leverages user behavior signals and ratings to make personalized movie recommendations. The approach considers user signals of enjoying movies and generates recommendations based on users' liking of specific movies."]}, {'end': 24708.716, 'start': 24502.341, 'title': 'Collaborative filtering algorithms in r', 'summary': 'Explains the concepts of user-based and item-based collaborative filtering, focusing on identifying frequently bought products and establishing recommendations based on user preferences and item co-occurrence in transactions, highlighting the differences and optimization parameters for both algorithms.', 'duration': 206.375, 'highlights': ['Item-based collaborative filtering identifies frequently bought products and establishes recommendations based on user preferences and item co-occurrence in transactions, showing a high correlation between items, enabling the suggestion of closely associated products as recommendations. Item-based collaborative filtering focuses on identifying frequently bought products and establishing recommendations based on user preferences and item co-occurrence in transactions, showing a high correlation between items, enabling the suggestion of closely associated products as recommendations.', 'User-based collaborative filtering looks at the co-occurrence of items in the same transaction or session to establish the set of items with a tendency of frequently being bought together, providing recommendations based on user preferences. User-based collaborative filtering examines the co-occurrence of items in transactions to establish the set of items with a tendency of frequently being bought together, providing recommendations based on user preferences.', 'Understanding the differences and optimization parameters for user-based and item-based collaborative filtering is essential before implementing both algorithms in R. Understanding the differences and optimization parameters for user-based and item-based collaborative filtering is essential before implementing both algorithms in R.']}, {'end': 25143.754, 'start': 24708.716, 'title': 'User-based collaborative filtering', 'summary': 'Explains user-based collaborative filtering, which identifies similar users to make product recommendations, and how prediction scores are generated based on user similarity and product ratings, with potential impacts on recommendation accuracy and user engagement.', 'duration': 435.038, 'highlights': ['User-based collaborative filtering identifies similar users to make product recommendations. The system finds similarity between users based on their purchasing behavior and recommends products that similar users have liked, potentially increasing user engagement and satisfaction.', 'Prediction scores are generated based on user similarity and product ratings. The prediction score is calculated by the summation of the product of the rating given by similar users and the similarity index, impacting the accuracy of product recommendations and user engagement.', 'User engagement signals, such as likes, comments, and video retention, can be used to predict user ratings. User engagement signals, like user reactions, video retention, and multiple views, can be used to predict user ratings, potentially improving the accuracy of recommendations and user satisfaction.']}, {'end': 25736.227, 'start': 25143.774, 'title': 'Recommendation engine algorithm', 'summary': 'Introduces the process of generating movie recommendations using a recommendation engine, including calculating user similarity based on movie ratings and the formula for similarity calculation.', 'duration': 592.453, 'highlights': ['The process of generating movie recommendations using a recommendation engine involves calculating user similarity based on movie ratings and the formula for similarity calculation. The chapter discusses the process of generating movie recommendations using a recommendation engine, including calculating user similarity based on movie ratings and the formula for similarity calculation.', 'The importance of mean user rating in normalizing movie ratings to account for differences in rating behavior among users is emphasized. The chapter emphasizes the importance of mean user rating in normalizing movie ratings to account for differences in rating behavior among users.', 'The formula for similarity calculation, which is similar to correlation, is explained, indicating that the similarity value ranges between -1 and 1, with higher values indicating more similar user tastes. The chapter explains the formula for similarity calculation, similar to correlation, and highlights that the similarity value ranges between -1 and 1, with higher values indicating more similar user tastes.']}, {'end': 26232.206, 'start': 25737.248, 'title': 'Collaborative filtering: users and items', 'summary': 'Explains the process of user-based and item-based collaborative filtering, illustrating how to measure similarity between users and items, and generate recommendations based on user preferences and frequently bought products.', 'duration': 494.958, 'highlights': ['User A and user C have given the absolute same rating to common movies, resulting in a similarity of one, while the similarity between user B and user C is -0.86, indicating highly dissimilar tastes. User A and user C have a similarity of one due to identical ratings, while the similarity between user B and user C is -0.86, reflecting contrasting preferences.', 'The user-based collaborative filter recommends movies liked by similar users, such as showing a high-rated movie by user A as a recommendation to user C. Highly similar users receive recommendations based on their preferences, like suggesting a movie liked by user A to user C.', 'In item-based collaborative filtering, recommendations are made based on products frequently bought together, using the concept of support, confidence, and lift to identify and recommend complementary products. Item-based collaborative filtering utilizes association rule mining to recommend products frequently bought together, leveraging support, confidence, and lift as indicators.', 'Considering users who consistently rate the same is important in the analysis, but users with consistent ratings may also indicate bots or noisy data, which can be addressed by filtering and data cleaning. Consistently rated users are included in the analysis, but consistent ratings may signal bots or noisy data, necessitating filtering and data cleaning.']}], 'duration': 1897.502, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk24334704.jpg', 'highlights': ['User-based collaborative filtering identifies similar users and recommends items based on their preferences and behavior signals.', 'Item-based collaborative filtering calculates item similarity and recommends top similar items based on user preferences.', 'Leverages user behavior signals and ratings to make personalized movie recommendations.', 'Item-based collaborative filtering identifies frequently bought products and establishes recommendations based on user preferences and item co-occurrence in transactions.', 'User-based collaborative filtering looks at the co-occurrence of items in the same transaction or session to establish the set of items with a tendency of frequently being bought together.', 'Understanding the differences and optimization parameters for user-based and item-based collaborative filtering is essential before implementing both algorithms in R.', 'User-based collaborative filtering identifies similar users to make product recommendations.', 'Prediction scores are generated based on user similarity and product ratings.', 'User engagement signals, such as likes, comments, and video retention, can be used to predict user ratings.', 'The process of generating movie recommendations using a recommendation engine involves calculating user similarity based on movie ratings and the formula for similarity calculation.', 'The importance of mean user rating in normalizing movie ratings to account for differences in rating behavior among users is emphasized.', 'The formula for similarity calculation, which is similar to correlation, is explained, indicating that the similarity value ranges between -1 and 1, with higher values indicating more similar user tastes.', 'User A and user C have given the absolute same rating to common movies, resulting in a similarity of one, while the similarity between user B and user C is -0.86, indicating highly dissimilar tastes.', 'The user-based collaborative filter recommends movies liked by similar users, such as showing a high-rated movie by user A as a recommendation to user C.', 'In item-based collaborative filtering, recommendations are made based on products frequently bought together, using the concept of support, confidence, and lift to identify and recommend complementary products.', 'Considering users who consistently rate the same is important in the analysis, but users with consistent ratings may also indicate bots or noisy data, which can be addressed by filtering and data cleaning.']}, {'end': 27136.951, 'segs': [{'end': 26711.923, 'src': 'embed', 'start': 26677.95, 'weight': 1, 'content': [{'end': 26679.07, 'text': 'you will look at all of those things.', 'start': 26677.95, 'duration': 1.12}, {'end': 26686.113, 'text': "right to identify the metadata of the of the content right, but in this case you're not really looking at the metadata of the content.", 'start': 26679.07, 'duration': 7.043}, {'end': 26687.594, 'text': "rather, you're just looking at.", 'start': 26686.113, 'duration': 1.481}, {'end': 26689.374, 'text': "you're just looking at the ratings right.", 'start': 26687.594, 'duration': 1.78}, {'end': 26695.637, 'text': 'so if a user has watched and liked a movie, irrespective of whether that movie is of the same genre, same language, same actor, etc.', 'start': 26689.374, 'duration': 6.263}, {'end': 26698.037, 'text': "it doesn't matter Someone.", 'start': 26695.637, 'duration': 2.4}, {'end': 26702.979, 'text': 'just giving a rating is an indicator of whether they like or whether they do not like that.', 'start': 26698.037, 'duration': 4.942}, {'end': 26705.28, 'text': "So that's essentially that's all that is used.", 'start': 26703.48, 'duration': 1.8}, {'end': 26711.923, 'text': "It's essentially an algorithm which looks at a very granular information,", 'start': 26705.52, 'duration': 6.403}], 'summary': 'Analyzing user ratings to determine preferences regardless of content metadata.', 'duration': 33.973, 'max_score': 26677.95, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk26677950.jpg'}, {'end': 26878.138, 'src': 'embed', 'start': 26850.699, 'weight': 3, 'content': [{'end': 26858.063, 'text': 'then, once the data set, once that data structure is created, okay, once you have that model, the data model ready,', 'start': 26850.699, 'duration': 7.364}, {'end': 26862.866, 'text': 'right after that implementing the concept and then getting recommendation out of it.', 'start': 26858.063, 'duration': 4.803}, {'end': 26868.651, 'text': "it's probably the simpler part, okay, of the recommendation engines.", 'start': 26862.866, 'duration': 5.785}, {'end': 26878.138, 'text': 'so what you need to do is you need to have two data sets available to you, right, two data sets that you should have in your, in your library.', 'start': 26868.651, 'duration': 9.487}], 'summary': 'Creating data structure and model, then implementing recommendation concept using two data sets.', 'duration': 27.439, 'max_score': 26850.699, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk26850699.jpg'}, {'end': 27004.272, 'src': 'embed', 'start': 26979.378, 'weight': 4, 'content': [{'end': 26986.539, 'text': 'okay, the language of the book, the average rating of that book, the total number of users who have rated that book.', 'start': 26979.378, 'duration': 7.161}, {'end': 26993.087, 'text': 'so a lot of different pieces of information with respect to the book itself is available in the books dataset.', 'start': 26986.539, 'duration': 6.548}, {'end': 27001.071, 'text': 'but what we need, what we essentially need from this dataset, is just Okay, guys, a quick info.', 'start': 26993.087, 'duration': 7.984}, {'end': 27004.272, 'text': "if you're looking for an end-to-end certification in data science,", 'start': 27001.071, 'duration': 3.201}], 'summary': 'Data science dataset contains book language, average rating, and user ratings.', 'duration': 24.894, 'max_score': 26979.378, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk26979378.jpg'}, {'end': 27136.951, 'src': 'embed', 'start': 27088.292, 'weight': 0, 'content': [{'end': 27091.013, 'text': 'it will give you some idea around.', 'start': 27088.292, 'duration': 2.721}, {'end': 27101.698, 'text': 'it will basically be used for converting the data frame to several types of matrices, which is required, which is an additional requirement here,', 'start': 27091.013, 'duration': 10.685}, {'end': 27109.756, 'text': 'and and What you also have to use is a library called the Recommender Lab.', 'start': 27101.698, 'duration': 8.058}, {'end': 27116.085, 'text': 'The Recommender Lab is the package of the library to generate recommendations, to build your recommendation model.', 'start': 27109.917, 'duration': 6.168}, {'end': 27118.726, 'text': 'you need this library called recommender lab.', 'start': 27116.846, 'duration': 1.88}, {'end': 27121.607, 'text': 'okay, so i think you would already.', 'start': 27118.726, 'duration': 2.881}, {'end': 27125.928, 'text': 'you would have only this library out of the four libraries which i mentioned.', 'start': 27121.607, 'duration': 4.321}, {'end': 27128.629, 'text': 'you would only have dplyr installed with you already.', 'start': 27125.928, 'duration': 2.701}, {'end': 27129.749, 'text': 'you should library.', 'start': 27128.629, 'duration': 1.12}, {'end': 27136.211, 'text': 'you should run install.packages okay, and then, within that, the three libraries that you need to install.', 'start': 27129.749, 'duration': 6.462}, {'end': 27136.951, 'text': 'okay, you should have.', 'start': 27136.211, 'duration': 0.74}], 'summary': 'Using recommender lab to generate recommendation model and converting data frame to matrices.', 'duration': 48.659, 'max_score': 27088.292, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk27088292.jpg'}], 'start': 26232.206, 'title': 'Collaborative filtering in recommendation systems', 'summary': 'Covers item-based collaborative filtering, calculating movie similarity, different approaches in collaborative filtering, and building a book recommendation engine with six recommendations per user.', 'chapters': [{'end': 26411.397, 'start': 26232.206, 'title': 'Item-based collaborative filtering', 'summary': 'Explains item-based collaborative filtering, highlighting its similarity to user-based collaborative filtering, the process of finding similarity between items, and the use of item similarity to generate recommendations.', 'duration': 179.191, 'highlights': ['Item-based collaborative filtering is similar to user-based collaborative filtering but focuses on finding similarity between items rather than users, using the same formula for similarity calculation. The chapter highlights that item-based collaborative filtering is mostly similar to user-based collaborative filtering, with the focus on finding similarity between items and using the same formula for similarity calculation.', 'The process involves finding similarity between items to identify which items are frequently watched together, enabling the generation of recommendations based on item preferences rather than user preferences. The process involves finding similarity between items to identify which items are frequently watched together, enabling the generation of recommendations based on item preferences rather than user preferences.', 'Utilizing item similarity, recommendations can be generated by identifying a highly similar item to the one already watched and suggesting it as a recommendation. Utilizing item similarity, recommendations can be generated by identifying a highly similar item to the one already watched and suggesting it as a recommendation.']}, {'end': 26594.825, 'start': 26411.397, 'title': 'Calculating movie similarity', 'summary': 'Discusses the process of calculating similarity between movies based on user ratings and demonstrates the application of the method with examples, ultimately providing recommendations for movie selection.', 'duration': 183.428, 'highlights': ['Movies with identical user ratings have very high similarity, making them ideal for recommendation. The example demonstrates that movies with similar user ratings have a very high similarity, making them ideal for recommendation.', 'The similarity score between movie 1 and movie 5 is 0.94, indicating a good level of similarity. The similarity score between movie 1 and movie 5 is 0.94, indicating a good level of similarity based on user ratings.', 'The process involves calculating the mean item rating for each movie based on user ratings. The method involves calculating the mean item rating for each movie based on the different ratings given by users.']}, {'end': 26754.633, 'start': 26594.825, 'title': 'Collaborative filtering approaches', 'summary': 'Explains the difference between user-based and item-based collaborative filtering, highlighting how item-based filtering analyzes similarities between items, and user-based filtering focuses on ratings rather than user attributes.', 'duration': 159.808, 'highlights': ['Item-based collaborative filtering analyzes similarities between items, like genre, language, and actors, providing a granular and specific approach to recommendations.', 'User-based collaborative filtering focuses on ratings rather than user attributes, providing a simple analysis based on user preferences.', 'Hybrid models can combine collaborative filtering with other approaches to incorporate additional attributes like movie language and actors for a more comprehensive analysis.', 'Collaborative filtering looks at the ratings to calculate similarity scores and predict item recommendations based on user preferences.']}, {'end': 27136.951, 'start': 26754.633, 'title': 'Building book recommendation engine', 'summary': 'Covers the process of building a book recommendation engine using two main datasets, with an emphasis on data manipulation and the use of specific libraries, to generate around six recommendations for each user.', 'duration': 382.318, 'highlights': ['The chapter covers the process of building a book recommendation engine using two main datasets Emphasizes the use of two main datasets - ratings dataset and books dataset - for building the recommendation engine.', 'Emphasis on data manipulation and the use of specific libraries Highlights the importance of data manipulation and the use of specific libraries such as dplyr, tidyverse, matrix, and Recommender Lab.', 'Generating around six recommendations for each user Specifies the goal of generating approximately six book recommendations for each user in the dataset.']}], 'duration': 904.745, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk26232206.jpg', 'highlights': ['Item-based collaborative filtering focuses on finding similarity between items and using the same formula for similarity calculation.', 'The process involves finding similarity between items to identify which items are frequently watched together, enabling the generation of recommendations based on item preferences rather than user preferences.', 'Utilizing item similarity, recommendations can be generated by identifying a highly similar item to the one already watched and suggesting it as a recommendation.', 'Movies with identical user ratings have very high similarity, making them ideal for recommendation.', 'The process involves calculating the mean item rating for each movie based on user ratings.', 'Item-based collaborative filtering analyzes similarities between items, like genre, language, and actors, providing a granular and specific approach to recommendations.', 'Emphasis on data manipulation and the use of specific libraries such as dplyr, tidyverse, matrix, and Recommender Lab.', 'Generating around six recommendations for each user.']}, {'end': 28847.462, 'segs': [{'end': 27518.189, 'src': 'embed', 'start': 27486.813, 'weight': 2, 'content': [{'end': 27487.191, 'text': '1, 2, 3, 4, 5, 6, 7.', 'start': 27486.813, 'duration': 0.378}, {'end': 27492.836, 'text': 'And then each row represents one book, right? This 2, 3, 4, 5, 7, 8, 9, etc.', 'start': 27487.193, 'duration': 5.643}, {'end': 27495.757, 'text': 'The labels of the columns is nothing but the book ID.', 'start': 27493.056, 'duration': 2.701}, {'end': 27498.178, 'text': 'Okay This 14 is a book.', 'start': 27497.018, 'duration': 1.16}, {'end': 27499.079, 'text': '15 is a book.', 'start': 27498.198, 'duration': 0.881}, {'end': 27506.222, 'text': '18 is a book, right? For example, user number one has not rated any of these books, right? The first 25 books that I have in my data.', 'start': 27499.099, 'duration': 7.123}, {'end': 27511.606, 'text': 'But user ID 3 has rated the book 4 and has given a rating 3 to it.', 'start': 27507.165, 'duration': 4.441}, {'end': 27518.189, 'text': 'So wherever you see an NA, it means that that user has not rated that book.', 'start': 27511.927, 'duration': 6.262}], 'summary': 'Data includes 25 books with user ratings; 3 rated book 4 with 3 stars.', 'duration': 31.376, 'max_score': 27486.813, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk27486813.jpg'}, {'end': 27659.633, 'src': 'embed', 'start': 27632.149, 'weight': 1, 'content': [{'end': 27635.491, 'text': 'and each number representing the rating that that user has given to that book.', 'start': 27632.149, 'duration': 3.342}, {'end': 27640.705, 'text': "Okay What you're going to do next, what we're going to do next.", 'start': 27637.232, 'duration': 3.473}, {'end': 27645.367, 'text': 'Yeah, Why have I deleted the user ID?', 'start': 27642.146, 'duration': 3.221}, {'end': 27653.13, 'text': 'is because because in my rating matrix I only want the content which should be present in my dataset right?', 'start': 27645.367, 'duration': 7.763}, {'end': 27657.952, 'text': 'The actual value that I have in my dataset on my matrix should only be the ratings right?', 'start': 27653.15, 'duration': 4.802}, {'end': 27659.633, 'text': 'I do not want any additional fields.', 'start': 27657.992, 'duration': 1.641}], 'summary': 'Creating a rating matrix with only essential data to capture user book ratings.', 'duration': 27.484, 'max_score': 27632.149, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk27632149.jpg'}, {'end': 28720.705, 'src': 'embed', 'start': 28690.23, 'weight': 0, 'content': [{'end': 28693.735, 'text': 'okay, so ubcf is what we are going to use first.', 'start': 28690.23, 'duration': 3.505}, {'end': 28696.979, 'text': 'right, and we can also build a model on the collaborate, on the item based collaborative filtering.', 'start': 28693.735, 'duration': 3.244}, {'end': 28698.942, 'text': 'but we will look at item based collaborative later.', 'start': 28696.979, 'duration': 1.963}, {'end': 28701.546, 'text': 'first we will do a ubcf model.', 'start': 28698.942, 'duration': 2.604}, {'end': 28705.311, 'text': 'okay, ubcf, it might take a couple of minutes for it to run.', 'start': 28701.546, 'duration': 3.765}, {'end': 28713.598, 'text': 'okay, And then what you have to specify is the total number of recommendations that you want to generate for each user.', 'start': 28705.311, 'duration': 8.287}, {'end': 28720.705, 'text': 'This recommendation will be done at a user level because you want to show these recommendations to the user on the website.', 'start': 28713.798, 'duration': 6.907}], 'summary': 'Using ubcf model to generate user recommendations for website, specifying total number of recommendations.', 'duration': 30.475, 'max_score': 28690.23, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk28690230.jpg'}, {'end': 28847.462, 'src': 'embed', 'start': 28823.831, 'weight': 5, 'content': [{'end': 28832.958, 'text': 'Otherwise, you can use an s-apply and you can change this value from 1 to the number of users that you have in your rec and disco test.', 'start': 28823.831, 'duration': 9.127}, {'end': 28834.359, 'text': 'Of course, very simple to do that.', 'start': 28833.378, 'duration': 0.981}, {'end': 28841.677, 'text': 'But yeah, at a code level, this code will only, the predict function will only work for one user at a time.', 'start': 28836.713, 'duration': 4.964}, {'end': 28847.462, 'text': 'Okay So what has it generated? It has generated direct underscore by iterator underscore UVCA.', 'start': 28842.978, 'duration': 4.484}], 'summary': 'The predict function works for one user at a time, generating direct_by_iterator_uvca.', 'duration': 23.631, 'max_score': 28823.831, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk28823831.jpg'}], 'start': 27136.951, 'title': 'Data manipulation and matrix transformation', 'summary': 'Covers the generation of a real rating matrix with 1806 unique users and 5033 unique books, transforming data into matrix format, compressing data into a sparse matrix, and implementing user-based collaborative filtering with an 80-20 split for training and testing sets.', 'chapters': [{'end': 27539.532, 'start': 27136.951, 'title': 'Data manipulation and matrix generation', 'summary': "Covers the process of generating unique user and book ids, creating a real rating matrix with 1806 unique users and 5033 unique books, and using the 'spread' function to transform the data structure from long to wide format.", 'duration': 402.581, 'highlights': ['Creating a real rating matrix with 1806 unique users and 5033 unique books The dataset contains 1806 unique users and 5033 unique books, forming the basis for a real rating matrix for building a recommendation engine.', "Using the 'spread' function to transform data structure from long to wide format The 'spread' function from the 'tidy wire' library is used to transpose the data structure, converting it from long to wide format to represent each row as unique to a user and each column as unique to a book.", 'Process of generating unique user and book IDs The process involves generating a list of unique user IDs and a list of unique book IDs, sorted in ascending order, from the ratings dataset.']}, {'end': 27887.609, 'start': 27539.532, 'title': 'Data matrix transformation', 'summary': 'Covers the process of transforming a data set into a matrix format, removing user identifiers and assigning row and column names, resulting in a matrix with 1806 users and 5033 books rated.', 'duration': 348.077, 'highlights': ['The matrix is created by converting the data set into a matrix format, resulting in a matrix with 1806 users and 5033 books rated. Matrix with 1806 users and 5033 books.', 'The user ID column is removed from the rating matrix to only retain numeric, quantitative weight ratings. User ID removed to retain only numeric ratings.', 'Row and column names are assigned to the matrix using the dimension names object, providing a way to identify users and books in the matrix. Assigning row and column names to the matrix.']}, {'end': 28410.44, 'start': 27888.35, 'title': 'Creating real rating matrix', 'summary': 'Discusses the process of creating a real rating matrix, including converting a simple matrix to a specialized form, using functions to handle missing data, and optimizing for efficiency by compressing data into a sparse matrix and then into a real rating matrix, resulting in a storage reduction from 73.2 mb to 1.7 mb.', 'duration': 522.09, 'highlights': ['Converting a simple matrix into a real rating matrix is essential for building a recommendation engine, requiring specialized forms of matrices. The process of creating a real rating matrix is crucial for building a recommendation engine, necessitating the conversion from a simple matrix to a specialized form.', "Using the function 'is.na' to convert missing data in the rating matrix to zeros, ensuring data integrity and usability for modeling. The use of the 'is.na' function to convert missing data in the rating matrix to zeros is essential for maintaining data integrity and usability for modeling.", 'Converting the rating matrix into a sparse matrix for efficiency and space optimization, crucial for handling large datasets and optimizing code. The conversion of the rating matrix into a sparse matrix is vital for efficiency and space optimization, especially when working with large datasets and optimizing code.', 'The transformation of the sparse matrix into a real rating matrix results in a significant reduction in storage space, from 73.2 MB to 1.7 MB, ensuring efficient storage and operation. The transformation of the sparse matrix into a real rating matrix leads to a notable reduction in storage space, ensuring efficient storage and operation with a decrease from 73.2 MB to 1.7 MB.']}, {'end': 28847.462, 'start': 28413.686, 'title': 'User-based collaborative filtering', 'summary': 'Discusses the process of converting the data into a format for user-based collaborative filtering, splitting the data into training and testing sets (80-20 split), and building a user-based collaborative filtering model to generate recommendations for each user.', 'duration': 433.776, 'highlights': ['The data is split into training and testing sets with an 80-20 split. The chapter discusses the process of splitting the data into training and testing sets, with the option to choose an 80-20 split, resulting in a training dataset of 1.4 MB and a testing dataset of 618 MB.', "The method 'UBCF' is used to train the recommender function for user-based collaborative filtering. The method 'UBCF' is utilized to train the recommender function for user-based collaborative filtering, which identifies similarities between users and makes predictions based on these similarities and the ratings given by similar users to the books they have read and rated.", 'The prediction function is used to generate recommendations for each user at a user level. The prediction function is employed to generate recommendations for each user at a user level, allowing for the specification of the total number of recommendations to be generated for each user and the generation of predictions for each user individually.']}], 'duration': 1710.511, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk27136951.jpg', 'highlights': ['Creating a real rating matrix with 1806 unique users and 5033 unique books', 'Converting the rating matrix into a sparse matrix for efficiency and space optimization', 'The transformation of the sparse matrix into a real rating matrix results in a significant reduction in storage space, from 73.2 MB to 1.7 MB', 'The data is split into training and testing sets with an 80-20 split', "The method 'UBCF' is used to train the recommender function for user-based collaborative filtering", 'The prediction function is used to generate recommendations for each user at a user level']}, {'end': 31096.305, 'segs': [{'end': 29433.231, 'src': 'heatmap', 'start': 29023.952, 'weight': 1, 'content': [{'end': 29029.417, 'text': "the reason I'm converting this into a numeric format is because I want to join it back to this books data set,", 'start': 29023.952, 'duration': 5.465}, {'end': 29033.001, 'text': 'and in the books data set this book ID is also in the integer format.', 'start': 29029.417, 'duration': 3.584}, {'end': 29040.708, 'text': 'so if I want to join these two tables on the book ID, I want to have make sure that the data types are similar.', 'start': 29033.001, 'duration': 7.707}, {'end': 29043.591, 'text': "okay. so I'm going to convert this into numeric as well.", 'start': 29040.708, 'duration': 2.883}, {'end': 29047.921, 'text': 'okay. and then what i can do is i can do a left join.', 'start': 29043.591, 'duration': 4.33}, {'end': 29050.961, 'text': 'right. this left join is coming from this default function.', 'start': 29047.921, 'duration': 3.04}, {'end': 29053.382, 'text': 'by the way, right this detail in detail.', 'start': 29050.961, 'duration': 2.421}, {'end': 29054.222, 'text': 'you have this.', 'start': 29053.382, 'duration': 0.84}, {'end': 29060.864, 'text': 'uh, you have this flexibility to join, to do a left join, right join or whatever right.', 'start': 29054.222, 'duration': 6.642}, {'end': 29062.824, 'text': 'so left join, top idf.', 'start': 29060.864, 'duration': 1.96}, {'end': 29063.664, 'text': 'on my left,', 'start': 29062.824, 'duration': 0.84}, {'end': 29075.21, 'text': "my left table is the top idea in my right table and what i'm doing is i'm selecting i'm selecting these these fields from my books data set right.", 'start': 29063.664, 'duration': 11.546}, {'end': 29080.194, 'text': "i'm selecting the book id, the author name, the title, the language, the average day.", 'start': 29075.21, 'duration': 4.984}, {'end': 29084.878, 'text': "right. and i'm going to do the join on book id right, i have book id.", 'start': 29080.194, 'duration': 4.684}, {'end': 29087.1, 'text': 'in books data set, i have book id in top idea.', 'start': 29084.878, 'duration': 2.222}, {'end': 29088.641, 'text': 'i want to join both of them.', 'start': 29087.1, 'duration': 1.541}, {'end': 29092.184, 'text': 'right, and retain the information from both the tables.', 'start': 29088.641, 'duration': 3.543}, {'end': 29094.105, 'text': "okay, so that's what i'm doing here.", 'start': 29092.184, 'duration': 1.921}, {'end': 29100.084, 'text': 'right. so names, the names.', 'start': 29094.105, 'duration': 5.979}, {'end': 29103.206, 'text': 'this gets generated and if i do a view of names, this is the.', 'start': 29100.084, 'duration': 3.122}, {'end': 29107.069, 'text': 'this is the kind of output that i get.', 'start': 29103.206, 'duration': 3.863}, {'end': 29116.975, 'text': 'okay, these are the five books that have been, that have shown up as recommendations for this user.', 'start': 29107.069, 'duration': 9.906}, {'end': 29122.707, 'text': 'five, right, These are the authors.', 'start': 29116.975, 'duration': 5.732}, {'end': 29128.509, 'text': 'By the way, you also see that the couple of authors, Marge and Satrapi, is common between two books.', 'start': 29122.747, 'duration': 5.762}, {'end': 29131.971, 'text': "In fact, it's the same book, Persepolis and Persepolis II.", 'start': 29128.549, 'duration': 3.422}, {'end': 29134.917, 'text': 'which similar users.', 'start': 29133.376, 'duration': 1.541}, {'end': 29144.661, 'text': 'actually, this is also an identification of the fact that, when you look at similarity between users, it also sort of takes care of the genre,', 'start': 29134.917, 'duration': 9.744}, {'end': 29147.863, 'text': 'the kind of likeness that people have towards content right.', 'start': 29144.661, 'duration': 3.202}, {'end': 29154.066, 'text': 'so you see, there are two books right out of five, which are essentially the same genre, the same author right,', 'start': 29147.863, 'duration': 6.203}, {'end': 29156.168, 'text': "although you haven't really put that as an input right.", 'start': 29154.066, 'duration': 2.102}, {'end': 29158.269, 'text': 'you just looked at user preferences.', 'start': 29156.168, 'duration': 2.101}, {'end': 29163.974, 'text': 'you just said that i want to see that who are these users that have very similar preferences as me right,', 'start': 29158.269, 'duration': 5.705}, {'end': 29170.979, 'text': "who have similar taste as me with respect to the ratings that they're given to the books that they read right, and two books right,", 'start': 29163.974, 'duration': 7.005}, {'end': 29177.624, 'text': 'which are essentially from the same author right, which are also in probably in the same genre as well, right, same language,', 'start': 29170.979, 'duration': 6.645}, {'end': 29178.665, 'text': 'have come up as recommendations.', 'start': 29177.624, 'duration': 1.041}, {'end': 29184.387, 'text': 'Okay, so it also sort of takes care, and this is a good validation of that.', 'start': 29181.044, 'duration': 3.343}, {'end': 29186.849, 'text': 'And also the other thing that you should check right?', 'start': 29184.487, 'duration': 2.362}, {'end': 29189.231, 'text': 'There are two books that are in Persian right?', 'start': 29187.509, 'duration': 1.722}, {'end': 29192.834, 'text': 'So it means that this user is also reading a lot of books in Persian.', 'start': 29189.331, 'duration': 3.503}, {'end': 29197.843, 'text': "We haven't used all of these features in our model anywhere.", 'start': 29195.421, 'duration': 2.422}, {'end': 29204.787, 'text': "We haven't specified that we want to look at the language code, we want to look at the genre of the book, we want to look at the authors.", 'start': 29197.863, 'duration': 6.924}, {'end': 29209.65, 'text': 'But it just comes out naturally, because when you look for similar users,', 'start': 29205.427, 'duration': 4.223}, {'end': 29215.765, 'text': "the kind of books that you have overlap with when you're given a very high rating to one book,", 'start': 29209.65, 'duration': 6.115}, {'end': 29222.168, 'text': "let's say five books the other users who are given a very high rating to the same book are also users who have similar preferences, like you.", 'start': 29215.765, 'duration': 6.403}, {'end': 29226.089, 'text': 'They read similar kind of languages, they read similar types of books as you.', 'start': 29222.188, 'duration': 3.901}, {'end': 29233.012, 'text': 'So it sort of takes care of the genre, it takes care of the metadata of the book system.', 'start': 29226.469, 'duration': 6.543}, {'end': 29234.393, 'text': "And it's evident here.", 'start': 29233.613, 'duration': 0.78}, {'end': 29245.767, 'text': 'Also, you should see that the average rating of all of these books that have shown as predictions or recommendations for this user are also very highly rated books.', 'start': 29236.198, 'duration': 9.569}, {'end': 29253.115, 'text': 'All these books have rating which is greater than 4 and 4 out of 5 is generally decent rating.', 'start': 29245.968, 'duration': 7.147}, {'end': 29260.502, 'text': "So yeah, that's how you generate recommendations and then look at the recommendations finally to show up on your tile.", 'start': 29254.336, 'duration': 6.166}, {'end': 29265.209, 'text': 'you can quickly generate recommendation for another user and see how they turn out to be.', 'start': 29261.147, 'duration': 4.062}, {'end': 29269.792, 'text': "let's let's say we want to generate recommendation for maybe the 15th user.", 'start': 29265.209, 'duration': 4.583}, {'end': 29272.433, 'text': 'in my dataset i could do that.', 'start': 29269.792, 'duration': 2.641}, {'end': 29278.016, 'text': 'okay, the same set of steps will repeat top high list for each user.', 'start': 29272.433, 'duration': 5.583}, {'end': 29283.219, 'text': "all of this will repeat and then you will finally look at the names of the books that they'll that they want to.", 'start': 29278.016, 'duration': 5.203}, {'end': 29285.36, 'text': 'that you want to suggest.', 'start': 29283.219, 'duration': 2.141}, {'end': 29292.034, 'text': "right, these are different watchmen, cat's cradle, speaker for the dead.", 'start': 29285.36, 'duration': 6.674}, {'end': 29293.654, 'text': 'why last man?', 'start': 29292.034, 'duration': 1.62}, {'end': 29296.915, 'text': 'all of them, then, by the way, are, uh, fictional movies.', 'start': 29293.654, 'duration': 3.261}, {'end': 29300.016, 'text': 'right, fictional movies, which are in english, right.', 'start': 29296.915, 'duration': 3.101}, {'end': 29301.717, 'text': "the authors aren't common.", 'start': 29300.016, 'duration': 1.701}, {'end': 29304.558, 'text': "i don't see any common authors, but they're all in the same genre.", 'start': 29301.717, 'duration': 2.841}, {'end': 29308.879, 'text': "in the sense they're all fictional movies and the ratings are also really high.", 'start': 29304.558, 'duration': 4.321}, {'end': 29315.296, 'text': 'someone who has good knowledge of books will have will probably be able to appreciate this better.', 'start': 29308.879, 'duration': 6.417}, {'end': 29317.758, 'text': "I haven't read any of these books.", 'start': 29315.836, 'duration': 1.922}, {'end': 29320.561, 'text': "I don't really know if there are similar books.", 'start': 29318.579, 'duration': 1.982}, {'end': 29325.525, 'text': 'What I know though is that most of them are Watchmen is a fiction book.', 'start': 29320.601, 'duration': 4.924}, {'end': 29326.966, 'text': "Cat's Cradle is also fiction.", 'start': 29325.585, 'duration': 1.381}, {'end': 29329.949, 'text': 'Speaker of the Dead also sounds like a fiction book.', 'start': 29327.327, 'duration': 2.622}, {'end': 29333.703, 'text': 'Okay, so this is how we generate recommendations.', 'start': 29331.621, 'duration': 2.082}, {'end': 29340.068, 'text': "That's how you generate recommendations for using user-based collaborative filtering.", 'start': 29335.124, 'duration': 4.944}, {'end': 29347.613, 'text': 'I think what could make sense for us to do now is look at one example where we generate recommendations using item-based collaborative filtering.', 'start': 29340.128, 'duration': 7.485}, {'end': 29352.197, 'text': "That's also one of the ways through which you can generate recommendations.", 'start': 29347.633, 'duration': 4.564}, {'end': 29357.161, 'text': 'How you generate recommendations and how you extract features out of it is something we can look at in the mind.', 'start': 29352.237, 'duration': 4.924}, {'end': 29362.387, 'text': "But, uh, look, I'll just quickly look at the example that, uh, okay.", 'start': 29358.066, 'duration': 4.321}, {'end': 29368.388, 'text': "Yeah, So let's just quickly look at how you would generate, uh, a recommendation engine right?", 'start': 29363.307, 'duration': 5.081}, {'end': 29370.909, 'text': 'Using the item based collaborative filtering right?', 'start': 29368.648, 'duration': 2.261}, {'end': 29377.01, 'text': 'The only thing that changes in your execution when you do item based collaborative filtering is this right?', 'start': 29371.169, 'duration': 5.841}, {'end': 29381.131, 'text': "So the method that you're going to say that you want to specify, okay.", 'start': 29377.03, 'duration': 4.101}, {'end': 29383.932, 'text': 'In the case of user item based collaborative filtering is.', 'start': 29381.531, 'duration': 2.401}, {'end': 29391.008, 'text': 'that you will make in this code where you did the recommender function, you would just say method is equal to IBCF.', 'start': 29385.285, 'duration': 5.723}, {'end': 29404.454, 'text': 'When you do IBCF, instead of looking at similarity between users, the data will start looking at similarity between items, similarity between books.', 'start': 29392.208, 'duration': 12.246}, {'end': 29411.217, 'text': 'What are the two books that have very similar scores in terms of people rating them?', 'start': 29404.514, 'duration': 6.703}, {'end': 29416.605, 'text': "okay, so let's just generate that model, right?", 'start': 29412.984, 'duration': 3.621}, {'end': 29418.366, 'text': "uh, i'm creating my model here.", 'start': 29416.605, 'duration': 1.761}, {'end': 29422.387, 'text': 'it takes might take a little bit of time to get trained.', 'start': 29418.366, 'duration': 4.021}, {'end': 29425.508, 'text': 'okay, we can generate.', 'start': 29422.387, 'duration': 3.121}, {'end': 29429.23, 'text': 'we have to specify the number of recommendation that you want to generate.', 'start': 29425.508, 'duration': 3.722}, {'end': 29431.831, 'text': "right. let's say it's five.", 'start': 29429.23, 'duration': 2.601}, {'end': 29433.231, 'text': 'everything else, by the way, remains the same right.', 'start': 29431.831, 'duration': 1.4}], 'summary': 'Converting and joining datasets, generating recommendations using user-based collaborative filtering, and considering features like genre and language in the process.', 'duration': 409.279, 'max_score': 29023.952, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk29023952.jpg'}, {'end': 29329.949, 'src': 'embed', 'start': 29301.717, 'weight': 1, 'content': [{'end': 29304.558, 'text': "i don't see any common authors, but they're all in the same genre.", 'start': 29301.717, 'duration': 2.841}, {'end': 29308.879, 'text': "in the sense they're all fictional movies and the ratings are also really high.", 'start': 29304.558, 'duration': 4.321}, {'end': 29315.296, 'text': 'someone who has good knowledge of books will have will probably be able to appreciate this better.', 'start': 29308.879, 'duration': 6.417}, {'end': 29317.758, 'text': "I haven't read any of these books.", 'start': 29315.836, 'duration': 1.922}, {'end': 29320.561, 'text': "I don't really know if there are similar books.", 'start': 29318.579, 'duration': 1.982}, {'end': 29325.525, 'text': 'What I know though is that most of them are Watchmen is a fiction book.', 'start': 29320.601, 'duration': 4.924}, {'end': 29326.966, 'text': "Cat's Cradle is also fiction.", 'start': 29325.585, 'duration': 1.381}, {'end': 29329.949, 'text': 'Speaker of the Dead also sounds like a fiction book.', 'start': 29327.327, 'duration': 2.622}], 'summary': 'All movies discussed are fictional with high ratings. most are in the genre of fiction books.', 'duration': 28.232, 'max_score': 29301.717, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk29301717.jpg'}, {'end': 29411.217, 'src': 'embed', 'start': 29381.531, 'weight': 2, 'content': [{'end': 29383.932, 'text': 'In the case of user item based collaborative filtering is.', 'start': 29381.531, 'duration': 2.401}, {'end': 29391.008, 'text': 'that you will make in this code where you did the recommender function, you would just say method is equal to IBCF.', 'start': 29385.285, 'duration': 5.723}, {'end': 29404.454, 'text': 'When you do IBCF, instead of looking at similarity between users, the data will start looking at similarity between items, similarity between books.', 'start': 29392.208, 'duration': 12.246}, {'end': 29411.217, 'text': 'What are the two books that have very similar scores in terms of people rating them?', 'start': 29404.514, 'duration': 6.703}], 'summary': 'Implement item-based collaborative filtering for finding similar books.', 'duration': 29.686, 'max_score': 29381.531, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk29381531.jpg'}, {'end': 30254.284, 'src': 'embed', 'start': 30213.908, 'weight': 0, 'content': [{'end': 30220.152, 'text': "Going ahead we'll find the top 10 books with highest ratings and finally we'll find out the 10 most popular books.", 'start': 30213.908, 'duration': 6.244}, {'end': 30224.575, 'text': "Right so we are back to our studio again and it's time for phase 2 now.", 'start': 30221.072, 'duration': 3.503}, {'end': 30229.798, 'text': 'So the first task in our phase 2 was to select a sample from the entire data set.', 'start': 30225.175, 'duration': 4.623}, {'end': 30237.043, 'text': "So I'll go ahead and set a seed so that if I ever want to run these commands again I can get the same results.", 'start': 30230.739, 'duration': 6.304}, {'end': 30254.284, 'text': "So I'll set the seed value to be 1 and I'll set a user fraction of 0.02 that is from the entire user base I need only 2% of the sample users.", 'start': 30238.393, 'duration': 15.891}], 'summary': 'Identifying top 10 books by ratings and 10 most popular books from 2% sample users.', 'duration': 40.376, 'max_score': 30213.908, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk30213908.jpg'}, {'end': 30816.747, 'src': 'embed', 'start': 30786.677, 'weight': 3, 'content': [{'end': 30792.138, 'text': "after which I'll use the summarize function and get the number of counts of each of these tag IDs.", 'start': 30786.677, 'duration': 5.461}, {'end': 30795.699, 'text': "Or in other words, I'll get the count of the different genres.", 'start': 30792.618, 'duration': 3.081}, {'end': 30798.52, 'text': 'So let me hit enter and let me see what do we get.', 'start': 30796.459, 'duration': 2.061}, {'end': 30802.601, 'text': 'So this is the tag ID 2938.', 'start': 30799.14, 'duration': 3.461}, {'end': 30805.581, 'text': 'And for this corresponding genre, the count is 436.', 'start': 30802.601, 'duration': 2.98}, {'end': 30807.782, 'text': 'That is there are 436 books belonging to this genre.', 'start': 30805.581, 'duration': 2.201}, {'end': 30816.747, 'text': 'Similarly, this is the tag ID 4605 and the count is 1109.', 'start': 30811.823, 'duration': 4.924}], 'summary': 'Using the summarize function, the count of tag ids 2938 and 4605 are 436 and 1109 respectively.', 'duration': 30.07, 'max_score': 30786.677, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk30786677.jpg'}], 'start': 28847.482, 'title': 'Book and user recommendations', 'summary': 'Covers techniques for generating book recommendations, including collaborative filtering, resulting in 980,000 ratings for 10,000 books from 53,424 users. it also explores user recommendation generation, genre distribution analysis, and identification of top 10 highest rated books.', 'chapters': [{'end': 29043.591, 'start': 28847.482, 'title': 'Generating book recommendations', 'summary': 'Discusses extracting book recommendations for users, mapping book ids to original dataset, and converting data types for further analysis, aiming to identify the recommended books and their details.', 'duration': 196.109, 'highlights': ['The chapter discusses extracting book recommendations for users, mapping book IDs to original dataset, and converting data types for further analysis, aiming to identify the recommended books and their details.', 'The user has been recommended the following book IDs: 4065, 3690, 4317, 591, and 1547.', "The process involves converting the top5dl object into a data frame and renaming the column to 'book ID' for easier manipulation and analysis.", 'The speaker emphasizes the importance of converting the data into numeric format to ensure compatibility for joining with the books dataset for further analysis.']}, {'end': 29278.016, 'start': 29043.591, 'title': 'Generating user recommendations', 'summary': 'Explains how to generate user recommendations by performing a left join to retain information from both tables and highlights the identification of similar preferences, validation of the model, and the natural emergence of features like language code and genre without explicit specification, and the high average ratings of the recommended books.', 'duration': 234.425, 'highlights': ["The identification of similar preferences is demonstrated by the presence of two books from the same author and genre among the five recommended books, which validates the model's ability to consider user likeness and genre without explicit input.", "The emergence of features like language code and genre without explicit specification is evident, showcasing the model's ability to naturally incorporate such attributes when identifying similar user preferences.", 'The recommended books have high average ratings, with all books having a rating greater than 4 out of 5, indicating the quality of the recommendations and the preference for highly-rated books.', 'Performing a left join allows the retention of information from both tables, enabling the selection of specific fields from the books data set and facilitating the generation of recommendations for users based on their preferences and likeness.', 'The process of generating recommendations involves selecting specific fields from the books data set and joining the top idea with the left table, which results in the output of five recommended books for the user, demonstrating the functionality of the recommendation system.']}, {'end': 29470.58, 'start': 29278.016, 'title': 'Generating book recommendations', 'summary': 'Discusses how to generate book recommendations using collaborative filtering techniques, including user-based and item-based methods, and the time complexity involved in the process.', 'duration': 192.564, 'highlights': ['Item-based collaborative filtering involves finding similarities between items, such as books, based on user ratings, and can be used to generate recommendations for users.', 'The time complexity of finding similarities between items using item-based collaborative filtering is high, as it requires evaluating combinations of items, which can take a significant amount of time even on high-performance machines.', "The discussed books for generating recommendations include 'Watchmen', 'Cat's Cradle', and 'Speaker for the Dead', all of which are highly-rated fictional books in English, providing a basis for collaborative filtering recommendations.", 'The recommendation engine can be configured to specify the number of recommendations to generate, with the example suggesting the generation of five book recommendations based on item-based collaborative filtering.', "The method for specifying item-based collaborative filtering in the code involves setting the 'method' parameter to 'IBCF' in the recommender function, indicating the shift towards evaluating similarities between items instead of users."]}, {'end': 30633.046, 'start': 29473.447, 'title': 'Implementing collaborative filtering for book recommendations', 'summary': 'Details the process of implementing collaborative filtering for book recommendations, including data cleaning, sample selection, and generating distribution plots for ratings and number of ratings per book. the dataset comprises 980,000 ratings for 10,000 books from 53,424 users.', 'duration': 1159.599, 'highlights': ['The dataset comprises 980,000 ratings for 10,000 books from 53,424 users. Quantifiable data about the dataset, providing an overview of the size and scope of the data.', 'The dataset includes files such as ratings.csv, books.csv, booktags.csv, and tags.csv, each containing specific information about the books and user ratings. Highlights the different files in the dataset, providing context about the types of data being worked with.', 'The process involves data cleaning by removing duplicate ratings and users who have rated fewer than three books. Key step in data preparation, highlighting the actions taken to ensure data quality for the collaborative filtering process.', 'A sample set of 2% records from the entire dataset is extracted for data exploration and generating distribution plots for ratings and number of ratings per book. Describes the data exploration phase and the specific tasks involved in sample selection and visualization.']}, {'end': 31096.305, 'start': 30635.647, 'title': 'Book genre distribution and top 10 ratings', 'summary': 'Discusses the process of extracting and analyzing book genre data to create a percentage distribution plot, revealing fantasy as the most prevalent genre, and also identifies the top 10 highest rated books with the complete calvin and hobbes securing the highest average rating of 4.82.', 'duration': 460.658, 'highlights': ['The percentage distribution plot reveals fantasy as the most prevalent genre, with the least percentage belonging to cookbooks. Fantasy is the most prevalent genre in the dataset, while cookbooks have the lowest percentage. The plot provides a visual representation of the genre distribution.', "The highest rated book, The Complete Calvin and Hobbes, secures the highest average rating of 4.82. The book 'The Complete Calvin and Hobbes' has the highest average rating of 4.82, securing the top position in the list of highest rated books."]}], 'duration': 2248.823, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk28847482.jpg', 'highlights': ['The dataset comprises 980,000 ratings for 10,000 books from 53,424 users.', 'The percentage distribution plot reveals fantasy as the most prevalent genre, with the least percentage belonging to cookbooks.', 'The highest rated book, The Complete Calvin and Hobbes, secures the highest average rating of 4.82.', "The process involves converting the top5dl object into a data frame and renaming the column to 'book ID' for easier manipulation and analysis.", 'The identification of similar preferences is demonstrated by the presence of two books from the same author and genre among the five recommended books.']}, {'end': 32092.187, 'segs': [{'end': 31414.863, 'src': 'embed', 'start': 31368.061, 'weight': 2, 'content': [{'end': 31372.686, 'text': "So what I'm doing is I am manually removing this first column.", 'start': 31368.061, 'duration': 4.625}, {'end': 31376.406, 'text': "and I'm storing it back into rating mat.", 'start': 31373.823, 'duration': 2.583}, {'end': 31380.171, 'text': 'Now let me have a glance at first five rows and first five columns.', 'start': 31377.027, 'duration': 3.144}, {'end': 31383.776, 'text': 'Right So these are the first five rows and these are the first five columns.', 'start': 31380.451, 'duration': 3.325}, {'end': 31392.386, 'text': 'So these rows basically correspond to all of the user IDs and these columns basically correspond to all of the book IDs.', 'start': 31384.416, 'duration': 7.97}, {'end': 31395.489, 'text': 'so these any values which you see over here.', 'start': 31393.027, 'duration': 2.462}, {'end': 31403.074, 'text': 'so this basically means that the first user has not rated the first book, the first user has not rated the second book.', 'start': 31395.489, 'duration': 7.585}, {'end': 31407.237, 'text': 'similarly, the fourth user has not rated the third book, and so on.', 'start': 31403.074, 'duration': 4.163}, {'end': 31409.559, 'text': 'right, so we have our rating matrix ready.', 'start': 31407.237, 'duration': 2.322}, {'end': 31414.863, 'text': 'now let me also assign the dimension names to the dimension names of this rating mat object.', 'start': 31409.559, 'duration': 5.304}], 'summary': 'Manually removed first column, stored in rating mat, displayed first 5 rows and columns, and assigned dimension names.', 'duration': 46.802, 'max_score': 31368.061, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk31368061.jpg'}, {'end': 31474.067, 'src': 'embed', 'start': 31446.43, 'weight': 1, 'content': [{'end': 31451.633, 'text': 'So this basically signifies that all of the rows are represented by the user IDs.', 'start': 31446.43, 'duration': 5.203}, {'end': 31456.736, 'text': 'Similarly, if I go down, then we have all of the book IDs.', 'start': 31452.334, 'duration': 4.402}, {'end': 31460.439, 'text': 'So all of the columns are represented by the book IDs over here.', 'start': 31456.936, 'duration': 3.503}, {'end': 31467.303, 'text': 'Now let me use the dim function to find out the number of rows and columns in the metrics.', 'start': 31461.319, 'duration': 5.984}, {'end': 31474.067, 'text': 'So we see that there are 900 rows and 8,431 columns.', 'start': 31468.243, 'duration': 5.824}], 'summary': 'The dataset has 900 rows and 8,431 columns.', 'duration': 27.637, 'max_score': 31446.43, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk31446430.jpg'}, {'end': 31520.13, 'src': 'embed', 'start': 31496.741, 'weight': 0, 'content': [{'end': 31506.173, 'text': "So I will store this rating mat into a new object and name that object's name to be rating mat 0.", 'start': 31496.741, 'duration': 9.432}, {'end': 31513.106, 'text': 'Let me again have a glance at the number of dimensions of this dim of rating mat 0.', 'start': 31506.173, 'duration': 6.933}, {'end': 31515.307, 'text': 'So we have the same number of rows and columns.', 'start': 31513.106, 'duration': 2.201}, {'end': 31520.13, 'text': 'So again here the number of rows are 900 and the number of columns are 8431.', 'start': 31515.367, 'duration': 4.763}], 'summary': "Created a new object 'rating mat 0' with 900 rows and 8431 columns.", 'duration': 23.389, 'max_score': 31496.741, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk31496741.jpg'}], 'start': 31096.305, 'title': 'Popular books & collaborative filtering', 'summary': 'Unveils the top 10 most popular books, with the hunger games having the highest ratings count, followed by harry potter, twilight, and to kill a mockingbird. additionally, it discusses the plan to recommend six new books using a user-based collaborative filtering model. it also explains the process of transforming data for collaborative filtering, resulting in a real rating matrix with 900 rows, 8431 columns, and 18,832 ratings. furthermore, it details the process of building a user-based collaborative filtering model to recommend books.', 'chapters': [{'end': 31196.151, 'start': 31096.305, 'title': 'Top 10 most popular books', 'summary': 'Unveils the top 10 most popular books by arranging the ratings count column in descending order, revealing the hunger games as the most popular book with the highest ratings count, followed by harry potter, twilight, and to kill a mockingbird. additionally, the chapter discusses the plan to recommend six new books for two different readers using a user-based collaborative filtering model.', 'duration': 99.846, 'highlights': ['The Hunger Games is the most popular book with the highest ratings count. The most popular book in the list is The Hunger Games, which has the highest ratings count.', 'The plan to recommend six new books for two different readers using a user-based collaborative filtering model is discussed. The chapter discusses the plan to recommend six new books for two different readers using a user-based collaborative filtering model.']}, {'end': 31654.924, 'start': 31196.691, 'title': 'Transforming data for collaborative filtering', 'summary': 'Explains the process of transforming a data frame into a matrix for user-based collaborative filtering, involving extracting unique user and book ids, converting the data frame into a matrix, removing unnecessary columns, replacing na values with zeros, and converting the matrix into a sparse matrix and then into a real rating matrix, ultimately resulting in a real rating matrix with 900 rows, 8431 columns, and 18,832 ratings.', 'duration': 458.233, 'highlights': ['The process involves extracting unique user and book IDs from the data frame to form dimension names, converting the data frame into a wide format matrix, removing unnecessary columns, and converting the matrix into a real rating matrix with 900 rows and 8431 columns.', 'Replacing NA values with zeros in the real rating matrix, resulting in a sparse matrix that saves space and then converting the sparse matrix into a real rating matrix.', 'The final real rating matrix has 900 rows, 8431 columns, and a total of 18,832 ratings, ready for building a user-based collaborative filtering model.']}, {'end': 32092.187, 'start': 31657.591, 'title': 'Building user-based collaborative filtering model', 'summary': 'Explains the process of building a user-based collaborative filtering model to recommend books, including splitting the dataset into training and testing sets, building the model, and extracting book recommendations and their respective authors.', 'duration': 434.596, 'highlights': ['Using the sample function to create an 80-20 split for the dataset, where 80% of the observations comprise the training set and 20% make up the testing set. The sample function is used to split the dataset into training and testing sets with an 80-20 split, allowing for model training and evaluation.', 'Building a user-based collaborative filtering model using the recommender function with a recommendation of six books. The process involves building a user-based collaborative filtering model using the recommender function to recommend six books based on the training set.', 'Extracting the book recommendations and their respective authors based on the predicted values for specific users. The extraction involves finding and displaying the recommended books and their authors based on the predicted values for individual users, enhancing the user experience.']}], 'duration': 995.882, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk31096305.jpg', 'highlights': ['The Hunger Games is the most popular book with the highest ratings count.', 'The plan to recommend six new books for two different readers using a user-based collaborative filtering model is discussed.', 'The final real rating matrix has 900 rows, 8431 columns, and a total of 18,832 ratings, ready for building a user-based collaborative filtering model.', 'The process involves building a user-based collaborative filtering model using the recommender function to recommend six books based on the training set.', 'Using the sample function to split the dataset into training and testing sets with an 80-20 split, allowing for model training and evaluation.']}, {'end': 33842.727, 'segs': [{'end': 32117.284, 'src': 'embed', 'start': 32092.808, 'weight': 4, 'content': [{'end': 32100.783, 'text': 'Right guys, so we have successfully implemented the user-based collaborative filtering model and we have recommended six books to two different users.', 'start': 32092.808, 'duration': 7.975}, {'end': 32106.898, 'text': "hey guys, welcome back, and in today's session we'll be working on a project.", 'start': 32102.756, 'duration': 4.142}, {'end': 32109.279, 'text': "so let's have a look at the problem statement.", 'start': 32106.898, 'duration': 2.381}, {'end': 32117.284, 'text': 'so consider yourself to be the manager of a supermarket all mart and your task as the manager of the store would be to increase cross selling.', 'start': 32109.279, 'duration': 8.005}], 'summary': 'Implemented user-based collaborative filtering model, recommended 6 books to 2 users, working on increasing cross selling in a supermarket.', 'duration': 24.476, 'max_score': 32092.808, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk32092808.jpg'}, {'end': 32154.494, 'src': 'embed', 'start': 32127.709, 'weight': 1, 'content': [{'end': 32134.343, 'text': 'that is, you would want to figure out If a customer buys item A, how likely is he to buy item B as well?', 'start': 32127.709, 'duration': 6.634}, {'end': 32137.945, 'text': "So you'd have to start off by understanding the transactions.", 'start': 32135.183, 'duration': 2.762}, {'end': 32141.627, 'text': "So you'll have to find the total number of transactions.", 'start': 32138.505, 'duration': 3.122}, {'end': 32146.31, 'text': "Then you'll have to find the total number of items available in the inventory.", 'start': 32142.307, 'duration': 4.003}, {'end': 32154.494, 'text': "After which you'd have to find the total number of items purchased and then finally find out the 10 most frequently bought items.", 'start': 32147.09, 'duration': 7.404}], 'summary': 'Analyze customer behavior to identify frequent item associations and improve sales.', 'duration': 26.785, 'max_score': 32127.709, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk32127709.jpg'}, {'end': 32345.497, 'src': 'embed', 'start': 32315.161, 'weight': 2, 'content': [{'end': 32320.145, 'text': 'So that is why the number of columns which we have would also represent the total number of items.', 'start': 32315.161, 'duration': 4.984}, {'end': 32325.008, 'text': 'And since we have 22,346 columns, that would basically mean that we have 22,346 items in total in our inventory.', 'start': 32320.765, 'duration': 4.243}, {'end': 32333.575, 'text': 'And then we also have a value of density.', 'start': 32331.474, 'duration': 2.101}, {'end': 32340.176, 'text': 'So this density value basically gives the percentage of all of the non empty cells in our metrics, right?', 'start': 32334.195, 'duration': 5.981}, {'end': 32345.497, 'text': "And then we'd also have to find out the total number of items which were purchased.", 'start': 32340.796, 'duration': 4.701}], 'summary': 'Inventory consists of 22,346 items, with density representing non-empty cells and total purchased items.', 'duration': 30.336, 'max_score': 32315.161, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk32315161.jpg'}, {'end': 32977.614, 'src': 'embed', 'start': 32947.146, 'weight': 0, 'content': [{'end': 32953.472, 'text': 'So for this, the support value is 0.005 and the confidence value is 0.96.', 'start': 32947.146, 'duration': 6.326}, {'end': 32963.044, 'text': 'So again, here the support value of 0.005 means that out of all of the transactions, this item set is present 0.5% of the times.', 'start': 32953.472, 'duration': 9.572}, {'end': 32972.451, 'text': "And this confidence interval of 0.96 means that if someone buys Dolly girl children's cup and space boy children's bowl,", 'start': 32963.784, 'duration': 8.667}, {'end': 32977.614, 'text': "then he's 96% likely to also buy Dolly girl children's bowl.", 'start': 32972.451, 'duration': 5.163}], 'summary': 'Support value is 0.005 and confidence is 0.96, indicating 0.5% presence and 96% likelihood of purchase.', 'duration': 30.468, 'max_score': 32947.146, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk32947146.jpg'}, {'end': 33419.656, 'src': 'embed', 'start': 33394.775, 'weight': 3, 'content': [{'end': 33405.008, 'text': "Similarly, if someone buys jumbo shopper vintage red paisley, then there is only 30% likelihood that he'll also buy jumbo bag baroque black white.", 'start': 33394.775, 'duration': 10.233}, {'end': 33410.427, 'text': "Now let's go ahead and plot these rules.", 'start': 33408.345, 'duration': 2.082}, {'end': 33413.43, 'text': "So I'll start off by making a simple plot over here.", 'start': 33410.927, 'duration': 2.503}, {'end': 33416.333, 'text': "I'll use the plot function and I'll give in the rule 2 over here.", 'start': 33413.67, 'duration': 2.663}, {'end': 33419.656, 'text': 'Engine is HTML widget which gives us an interactive plot.', 'start': 33416.733, 'duration': 2.923}], 'summary': '30% likelihood of buying jumbo bag baroque black white with jumbo shopper vintage red paisley.', 'duration': 24.881, 'max_score': 33394.775, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk33394775.jpg'}], 'start': 32092.808, 'title': 'Association rule analysis', 'summary': 'Covers market basket analysis for cross-selling, identifying 10 most frequently bought items, and implementing user-based collaborative filtering model. it also discusses building association rules using the a priori algorithm with support value 0.005 and confidence value 0.8, sorting rules by confidence and lift, inspecting top and bottom rules, and analyzing association rules with a focus on item set length, support, and confidence.', 'chapters': [{'end': 32530.154, 'start': 32092.808, 'title': 'Market basket analysis project', 'summary': 'Introduces the concept of market basket analysis for increasing cross selling by finding associations between items, identifying 10 most frequently bought items, and implementing user-based collaborative filtering model.', 'duration': 437.346, 'highlights': ["The chapter introduces the concept of market basket analysis for increasing cross selling by finding associations between items. The manager's task is to encourage customers to buy related items by finding the association between different items, considering transactions and total number of items purchased.", "Identifying 10 most frequently bought items. The analysis reveals the 10 most frequently purchased items, with 'white hanging heart tea light holder' being the most frequently bought item.", 'Implementing user-based collaborative filtering model The chapter mentions the successful implementation of a user-based collaborative filtering model and recommendation of six books to two different users.']}, {'end': 33108.369, 'start': 32531.004, 'title': 'Association rules analysis', 'summary': 'Covers building association rules using the a priori algorithm with support value 0.005 and confidence value 0.8, sorting rules by confidence and lift, inspecting top and bottom five rules, and plotting the rules using different methods.', 'duration': 577.365, 'highlights': ['Built association rules using the a priori algorithm with support value 0.005 and confidence value 0.8 The support value of 0.005 means selecting item sets occurring at least 0.5% times, and the confidence value of 0.8 indicates that the consequence should be present 80% of the time.', "Inspected top five rules sorted by confidence and support values Identified top rules including 'garbage design' with 0.005 support and 1 confidence, 'elephant' with 0.006 support and 1 confidence, and 'retro spot' with 0.006 support and 1 confidence.", "Sorted rules with respect to lift and inspected the top 5 rules Explored rules like 'Dolly Girl Children's Cup and Space Boy Children's Bowl' with 0.005 support and 0.96 confidence, and 'pink Regency teacup and saucer and roses Regency teacup and saucer' with 0.007 support and 0.8 confidence.", "Plotted the rules using different methods including '2 key' and 'graph' Utilized methods to differentiate item sets by length and visualized graphical results for rule analysis."]}, {'end': 33447.745, 'start': 33115.125, 'title': 'Association rules analysis', 'summary': 'Explains the concept of association rules and demonstrates the process of building and analyzing association rules using a priori algorithm, support values, confidence values, and plotting methods, highlighting the top and bottom rules with specific support and confidence values.', 'duration': 332.62, 'highlights': ['The length of the item set could be either 2, 3, or 4, with 537 rules having an item set length of 2, 288 rules with a length of 3, and 16 rules with a length of 4. There are 537 rules with an item set length of 2, 288 rules with a length of 3, and 16 rules with a length of 4, demonstrating the distribution of rules based on the length of the item set.', "The rule with the highest confidence states that if someone buys Dolly Girl Children's Cup and Space Boy Children's Bowl, then he's 100% likely to buy Dolly Girl Children's Bowl, while the rule with the lowest confidence indicates only a 30% likelihood for a purchase of jumbo shopper vintage red paisley to be followed by a purchase of jumbo bag baroque black white. The highest confidence rule indicates a 100% likelihood for a specific purchase sequence, while the lowest confidence rule demonstrates a 30% likelihood for a different purchase sequence, highlighting varying levels of confidence in association rules.", 'The support values range from 0.011 to 0.013, indicating the percentage of times an item is bought in all transactions, while confidence values vary from 0.3 to 1, signifying the likelihood of a consequent purchase given an antecedent purchase. Support values ranging from 0.011 to 0.013 represent the frequency of specific item purchases, while confidence values from 0.3 to 1 depict the varying likelihood of consequent purchases based on antecedent purchases.']}, {'end': 33842.727, 'start': 33447.845, 'title': 'Association rule analysis', 'summary': 'Discusses the analysis of association rules with a focus on item set length, support, and confidence, resulting in 26 rules with item set length 2 and 3 rules with item set length 3, and the top rule having a support of 0.029 and confidence of 0.62.', 'duration': 394.882, 'highlights': ['The chapter discusses the analysis of association rules with a focus on item set length, support, and confidence, resulting in 26 rules with item set length 2 and 3 rules with item set length 3. (Item set length: 2 - 26 rules, Item set length: 3 - 3 rules)', 'The top rule has a support of 0.029 and confidence of 0.62, indicating the likelihood of a customer buying jumbo bag pink polka dot and jumbo bag red retro spot. (Support: 0.029, Confidence: 0.62)', 'The bottom rule reveals a support value of 0.02, signifying that out of all the transactions, 2% of the times alarm clock bake like pink was bought, with a 64% probability of also buying alarm clock bake like red. (Support: 0.02, Confidence: 0.64)', 'The analysis includes a graphical representation of association rules, highlighting the conditions for support and confidence values, with one rule showing a confidence of 0.78. (Confidence: 0.78)']}], 'duration': 1749.919, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk32092808.jpg', 'highlights': ["Identifying 10 most frequently bought items. Reveals 'white hanging heart tea light holder' as the most frequently bought item.", 'Built association rules using the a priori algorithm with support value 0.005 and confidence value 0.8, indicating the selection of item sets occurring at least 0.5% of the time and the consequence being present 80% of the time.', "Inspected top five rules sorted by confidence and support values, identifying top rules including 'garbage design' with 0.005 support and 1 confidence, 'elephant' with 0.006 support and 1 confidence, and 'retro spot' with 0.006 support and 1 confidence.", 'The length of the item set could be either 2, 3, or 4, with 537 rules having an item set length of 2, 288 rules with a length of 3, and 16 rules with a length of 4, demonstrating the distribution of rules based on the length of the item set.', "The rule with the highest confidence states that if someone buys Dolly Girl Children's Cup and Space Boy Children's Bowl, then he's 100% likely to buy Dolly Girl Children's Bowl, while the rule with the lowest confidence indicates only a 30% likelihood for a purchase of jumbo shopper vintage red paisley to be followed by a purchase of jumbo bag baroque black white.", 'The chapter discusses the analysis of association rules with a focus on item set length, support, and confidence, resulting in 26 rules with item set length 2 and 3 rules with item set length 3. (Item set length: 2 - 26 rules, Item set length: 3 - 3 rules)']}, {'end': 34739.422, 'segs': [{'end': 33967.375, 'src': 'embed', 'start': 33938.385, 'weight': 2, 'content': [{'end': 33945.991, 'text': 'After that data scientist must have knowledge of mathematics, specially calculus, linear algebra and statistics.', 'start': 33938.385, 'duration': 7.606}, {'end': 33948.693, 'text': 'Then comes data visualization skills.', 'start': 33946.611, 'duration': 2.082}, {'end': 33956.471, 'text': 'So R, Python, SAS and Tableau are widely used for data visualization tasks.', 'start': 33949.429, 'duration': 7.042}, {'end': 33962.473, 'text': 'So it depends on organization that whether they are comfortable with R or Python.', 'start': 33956.771, 'duration': 5.702}, {'end': 33967.375, 'text': 'Most of the organization uses Python and R for data visualization.', 'start': 33962.913, 'duration': 4.462}], 'summary': 'Data scientists need knowledge of calculus, linear algebra, and statistics, as well as skills in r, python, sas, and tableau for data visualization; python and r are commonly used.', 'duration': 28.99, 'max_score': 33938.385, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk33938385.jpg'}, {'end': 34059.022, 'src': 'embed', 'start': 34014.03, 'weight': 1, 'content': [{'end': 34020.212, 'text': 'So it completely depends upon the organization in which you are working or you are willing to work.', 'start': 34014.03, 'duration': 6.182}, {'end': 34022.514, 'text': 'so in which domain it is working.', 'start': 34020.632, 'duration': 1.882}, {'end': 34031.04, 'text': 'if it is a healthcare firm willing to hire a data scientist, so you must be aware of these clustering, classification,', 'start': 34022.514, 'duration': 8.526}, {'end': 34036.624, 'text': 'regression and boosting algorithm so that you can classify the data of the patients.', 'start': 34031.04, 'duration': 5.584}, {'end': 34044.87, 'text': 'and next, if you are willing to work in a mobile phone company where you will be involved in developing mobile phone software,', 'start': 34036.624, 'duration': 8.246}, {'end': 34049.194, 'text': 'then you must be aware of NLP concepts and how to implement them,', 'start': 34044.87, 'duration': 4.324}, {'end': 34059.022, 'text': 'because nowadays mobile phones are coming with voice recognition technology and voiceover search technologies, so you must be aware of that.', 'start': 34049.754, 'duration': 9.268}], 'summary': 'Data scientists need different skills depending on the industry; healthcare firms require clustering, classification, regression, and boosting algorithms, while mobile phone companies require nlp concepts and implementations.', 'duration': 44.992, 'max_score': 34014.03, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk34014030.jpg'}, {'end': 34232.107, 'src': 'embed', 'start': 34203.703, 'weight': 0, 'content': [{'end': 34206.605, 'text': 'So as we already know Python is in every field.', 'start': 34203.703, 'duration': 2.902}, {'end': 34211.569, 'text': 'So Python programming is used for development purpose either.', 'start': 34206.886, 'duration': 4.683}, {'end': 34220.016, 'text': 'it can be for web development, desktop application development, and it is extensively used in the field of machine learning,', 'start': 34211.569, 'duration': 8.447}, {'end': 34222.478, 'text': 'data science and artificial intelligence.', 'start': 34220.016, 'duration': 2.462}, {'end': 34232.107, 'text': 'so you can go for python data science course and certification by intellipaat in both ways either instructor-led live classes or self-paced training.', 'start': 34222.478, 'duration': 9.629}], 'summary': 'Python is used in various fields including web development, desktop applications, machine learning, data science, and artificial intelligence. intellipaat offers python data science course and certification through instructor-led live classes or self-paced training.', 'duration': 28.404, 'max_score': 34203.703, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk34203703.jpg'}], 'start': 33844.076, 'title': 'Essential skills and certifications for data scientists', 'summary': "Outlines the essential skills for a data scientist, including proficiency in r and python programming, knowledge of mathematics, data visualization, big data technologies, and machine learning algorithms, tailored to the industry's requirements. it also discusses the top 5 data science certifications for beginners, each providing foundational and advanced skills with lifetime validity, emphasizing the increasing demand for data science and its application in artificial intelligence-driven systems in today's world.", 'chapters': [{'end': 34083.922, 'start': 33844.076, 'title': 'Skills for data scientist', 'summary': "Outlines the essential skills for a data scientist, including proficiency in r and python programming, knowledge of mathematics, data visualization, big data technologies, and machine learning algorithms, tailored to the industry's requirements.", 'duration': 239.846, 'highlights': ['Data scientists must have proficiency in R and Python programming for statistical analysis, visualization, and machine learning, with R being recommended for data science tasks. Proficiency in R and Python is essential for statistical analysis, visualization, and machine learning tasks, with R being highly recommended by professionals and statisticians.', 'A strong understanding of mathematics, particularly calculus, linear algebra, and statistics, is crucial for data scientists. Data scientists must have a strong understanding of mathematics, particularly calculus, linear algebra, and statistics.', 'Proficiency in data visualization tools such as R, Python, SAS, and Tableau is essential, with Python and R being widely used, while SAS is utilized by companies for high-end software development. Proficiency in data visualization tools such as R, Python, SAS, and Tableau is essential, with Python and R widely used, and SAS being utilized by companies for high-end software development.', 'Thorough knowledge of big data technologies, including Hadoop, Spark, Apache Kafka, and Scala, is necessary for data scientists. Data scientists must have thorough knowledge of big data technologies, including Hadoop, Spark, Apache Kafka, and Scala.', 'Familiarity with machine learning algorithms such as classification, regression, clustering, and boosting is essential, tailored to the specific industry requirements, such as healthcare, mobile phone software development, and self-driving cars. Data scientists must be familiar with machine learning algorithms such as classification, regression, clustering, and boosting, tailored to specific industry requirements.']}, {'end': 34739.422, 'start': 34084.323, 'title': 'Top 5 data science certifications', 'summary': "Discusses the top 5 data science certifications for beginners, including data science online training & certification, python for data science, big data technologies, machine learning training & certification, and artificial intelligence training & certification, each providing foundational and advanced skills with lifetime validity, in order to make a career in data science, with an emphasis on the increasing demand for data science and its application in artificial intelligence-driven systems in today's world.", 'duration': 655.099, 'highlights': ['Data Science Online Training & Certification for beginners, includes R programming and advanced skills, with lifetime validity, to build a strong portfolio and provide comprehensive hands-on experience. This certification offers training in R programming, advanced skills, and machine learning algorithms, with lifetime validity, catering to beginners looking to build a strong portfolio and gain comprehensive hands-on experience.', 'Python for data science certification offers foundational and advanced skills, including Python libraries and machine learning algorithms, with lifetime validity, to nourish implementation skills and work on real-world projects. The certification covers Python for data science, foundational and advanced skills, Python libraries, and machine learning algorithms, with lifetime validity, focusing on nourishing implementation skills and working on real-world projects.', 'Big data technologies certification provides foundational and advanced skills in Hadoop, Spark, and Scala, with lifetime validity, enhancing skills in data analytics and processing of big data for developing algorithms. This certification offers training in big data technologies, including Hadoop, Spark, and Scala, with lifetime validity, focusing on enhancing skills in data analytics and processing big data for algorithm development.', 'Machine learning training & certification covers fundamental and advanced concepts, machine learning algorithms, statistics, and various Python libraries, with lifetime validity, essential for building effective strategies and automation of software. The machine learning certification covers fundamental and advanced concepts, machine learning algorithms, statistics, and Python libraries, with lifetime validity, emphasizing the importance of building effective strategies and software automation.', 'Artificial intelligence training & certification provides advanced and basic skills in deep learning algorithms, neural network TensorFlow, and deep learning framework, adding high value to the resume for data science interviews. This certification offers training in artificial intelligence, focusing on advanced and basic skills in deep learning algorithms, neural network TensorFlow, and deep learning framework, adding high value to resumes for data science interviews.']}], 'duration': 895.346, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk33844076.jpg', 'highlights': ['Proficiency in R and Python programming is essential for statistical analysis, visualization, and machine learning tasks, with R being highly recommended by professionals and statisticians.', 'Data scientists must have a strong understanding of mathematics, particularly calculus, linear algebra, and statistics.', 'Thorough knowledge of big data technologies, including Hadoop, Spark, Apache Kafka, and Scala, is necessary for data scientists.', 'Familiarity with machine learning algorithms such as classification, regression, clustering, and boosting is essential, tailored to specific industry requirements.', 'Data Science Online Training & Certification for beginners includes R programming and advanced skills, with lifetime validity, to build a strong portfolio and provide comprehensive hands-on experience.', 'Python for data science certification offers foundational and advanced skills, including Python libraries and machine learning algorithms, with lifetime validity, to nourish implementation skills and work on real-world projects.', 'Big data technologies certification provides foundational and advanced skills in Hadoop, Spark, and Scala, with lifetime validity, enhancing skills in data analytics and processing of big data for developing algorithms.', 'Machine learning training & certification covers fundamental and advanced concepts, machine learning algorithms, statistics, and various Python libraries, with lifetime validity, essential for building effective strategies and automation of software.', 'Artificial intelligence training & certification provides advanced and basic skills in deep learning algorithms, neural network TensorFlow, and deep learning framework, adding high value to the resume for data science interviews.']}, {'end': 36334.955, 'segs': [{'end': 34767.381, 'src': 'embed', 'start': 34739.422, 'weight': 3, 'content': [{'end': 34742.484, 'text': 'and of course, uh, to achieve this they have to use low power.', 'start': 34739.422, 'duration': 3.062}, {'end': 34745.885, 'text': "and then to use low power doesn't mean that they're compromising on stuff.", 'start': 34742.484, 'duration': 3.401}, {'end': 34750.427, 'text': "right at the end of the day, it's very intelligent, or even the most simple uh smart bands of today.", 'start': 34745.885, 'duration': 4.542}, {'end': 34756.97, 'text': 'so once we understand all of this, once we know that you know data science is the thing right now, data science is the future right now.', 'start': 34750.427, 'duration': 6.543}, {'end': 34762.696, 'text': 'And of course, I would urge all you dear viewers to, pretty much you know, jump on this trend train to make the best out of it.', 'start': 34757.15, 'duration': 5.546}, {'end': 34767.381, 'text': 'It can be a career, it can be those you know dream jobs that you guys are looking for, or,', 'start': 34762.976, 'duration': 4.405}], 'summary': 'Using low power is key for smart bands in data science for future career opportunities.', 'duration': 27.959, 'max_score': 34739.422, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk34739422.jpg'}, {'end': 34854.632, 'src': 'embed', 'start': 34825.081, 'weight': 0, 'content': [{'end': 34830.062, 'text': "So you might be wondering, okay, if they're not looking at degree, then what are they looking at? Well, let me tell you this.", 'start': 34825.081, 'duration': 4.981}, {'end': 34833.303, 'text': "You know if they're not looking at your degree or where you're coming from.", 'start': 34830.722, 'duration': 2.581}, {'end': 34839.404, 'text': "they would, of course, need another way where you can prove to them that you're actually good at what you do and for the role that you're applying.", 'start': 34833.303, 'duration': 6.101}, {'end': 34843.224, 'text': 'This comes with you generally, your knowledge, your learning.', 'start': 34839.824, 'duration': 3.4}, {'end': 34848.686, 'text': "You might be a person who's working a finance day job and at the, at the end of the day or at night,", 'start': 34843.304, 'duration': 5.382}, {'end': 34854.632, 'text': "you might be a very hardcore coder who's pretty much putting in all of the hours, all of the efforts, uh,", 'start': 34848.686, 'duration': 5.946}], 'summary': 'Employers focus on skills, not degrees. prove your expertise through knowledge and learning.', 'duration': 29.551, 'max_score': 34825.081, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk34825081.jpg'}, {'end': 35048.422, 'src': 'embed', 'start': 35021.165, 'weight': 6, 'content': [{'end': 35027.327, 'text': 'You need the assignments, you need the projects and you and you need everything else that will basically add to your knowledge to put,', 'start': 35021.165, 'duration': 6.162}, {'end': 35030.389, 'text': 'to put on a strong foot, saying that, yes, you have worked on it.', 'start': 35027.327, 'duration': 3.062}, {'end': 35032.051, 'text': 'And here is here is what I have done.', 'start': 35030.469, 'duration': 1.582}, {'end': 35033.151, 'text': 'And I am good at this.', 'start': 35032.271, 'duration': 0.88}, {'end': 35037.895, 'text': 'So, ladies and gentlemen, I hope you guys really understand the the situation at hand out here.', 'start': 35033.191, 'duration': 4.704}, {'end': 35039.416, 'text': "It's not all about learning every time.", 'start': 35037.915, 'duration': 1.501}, {'end': 35043.499, 'text': "It's about implementing something and solving the problems in today's world.", 'start': 35039.456, 'duration': 4.043}, {'end': 35048.422, 'text': 'And that can be done when you bring the best of both worlds with respect to knowledge and projects.', 'start': 35043.639, 'duration': 4.783}], 'summary': 'Balancing learning and projects is key to showcasing skills and solving real-world problems.', 'duration': 27.257, 'max_score': 35021.165, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk35021165.jpg'}, {'end': 35153.75, 'src': 'embed', 'start': 35120.583, 'weight': 8, 'content': [{'end': 35127.61, 'text': 'so, ladies and gentlemen, with statistics, concepts such as probability, concepts such as correlation and much more play a very vital aspect here.', 'start': 35120.583, 'duration': 7.027}, {'end': 35132.614, 'text': 'And then, if you have to talk about programming languages I just mentioned, okay, so we have Python, we have R,', 'start': 35127.87, 'duration': 4.744}, {'end': 35135.576, 'text': 'we have multiple other languages which are beautiful to work with.', 'start': 35132.614, 'duration': 2.962}, {'end': 35139.539, 'text': "And Python and R are in fact one of the top languages in today's world to learn.", 'start': 35135.756, 'duration': 3.783}, {'end': 35147.825, 'text': 'These languages are mostly used to, you know, go on to achieve machine learning, to achieve deep learning, to achieve good working of neural networks.', 'start': 35139.759, 'duration': 8.066}, {'end': 35153.75, 'text': 'And all of this correlates to what? Well, it basically correlates to achieving artificial intelligence when you think about it, ladies and gentlemen.', 'start': 35147.985, 'duration': 5.765}], 'summary': 'Python and r are top languages for machine learning and achieving artificial intelligence.', 'duration': 33.167, 'max_score': 35120.583, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk35120583.jpg'}, {'end': 35394.696, 'src': 'embed', 'start': 35369.7, 'weight': 2, 'content': [{'end': 35375.842, 'text': "and of course, uh, you might be thinking whoa, okay, that's a lot of skills, that's a lot of primary skills, it's a lot of secondary skills.", 'start': 35369.7, 'duration': 6.142}, {'end': 35376.623, 'text': "what's going on?", 'start': 35375.842, 'duration': 0.781}, {'end': 35379.584, 'text': 'well, ladies and gentlemen, again, uh, the most important thing.', 'start': 35376.623, 'duration': 2.961}, {'end': 35384.508, 'text': 'guys, please pay attention to this slide, because this is the most important thing that I can tell you.', 'start': 35379.904, 'duration': 4.604}, {'end': 35391.614, 'text': "it's all about focus when you talk about data science, because you know you cannot become an expert in all of the six concepts that I showed you.", 'start': 35384.508, 'duration': 7.106}, {'end': 35394.696, 'text': "then you will have to pick up something and you'll have to go through with that.", 'start': 35391.614, 'duration': 3.082}], 'summary': 'Focus on developing expertise in specific data science skills to excel.', 'duration': 24.996, 'max_score': 35369.7, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk35369700.jpg'}, {'end': 35428.448, 'src': 'embed', 'start': 35401.381, 'weight': 1, 'content': [{'end': 35406.987, 'text': 'so you have to pick your focus area to work with and then ensure that you know, you build upon that.', 'start': 35401.381, 'duration': 5.606}, {'end': 35410.791, 'text': 'you start with something which, of course, you might or might not know, and then you build on it.', 'start': 35406.987, 'duration': 3.804}, {'end': 35416.577, 'text': 'so this calls for you understanding, of course, all of the six primary skills to a certain beginner level.', 'start': 35410.791, 'duration': 5.786}, {'end': 35418.098, 'text': 'but then, when you have to get proficient,', 'start': 35416.577, 'duration': 1.521}, {'end': 35423.464, 'text': 'you need focus and you need that tunnel vision to ensure you pick up one thing and you are a master in that.', 'start': 35418.098, 'duration': 5.366}, {'end': 35425.406, 'text': 'ladies and gentlemen, i hope that point was clear.', 'start': 35423.464, 'duration': 1.942}, {'end': 35428.448, 'text': 'So, with that, you might be asking OK, so what is important?', 'start': 35425.646, 'duration': 2.802}], 'summary': 'To become proficient, focus on one area and master it.', 'duration': 27.067, 'max_score': 35401.381, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk35401381.jpg'}, {'end': 35957.816, 'src': 'embed', 'start': 35933.757, 'weight': 11, 'content': [{'end': 35942.244, 'text': 'But then the data analytics goal is to pretty much find some actionable data where you can make sense out of it and drive business decisions and more.', 'start': 35933.757, 'duration': 8.487}, {'end': 35946.128, 'text': 'And this brings us to all the major fields that we can go about using.', 'start': 35942.505, 'duration': 3.623}, {'end': 35950.791, 'text': "You might have heard of the term data science in majority of the fields in today's world.", 'start': 35946.368, 'duration': 4.423}, {'end': 35952.893, 'text': 'Well, it is used in machine learning.', 'start': 35951.091, 'duration': 1.802}, {'end': 35957.816, 'text': 'It is used to achieve artificial intelligence, you know, search engine engineering, corporate analytics.', 'start': 35952.973, 'duration': 4.843}], 'summary': 'Data analytics aims to derive actionable insights for business decisions using data science in various fields such as machine learning, artificial intelligence, search engine engineering, and corporate analytics.', 'duration': 24.059, 'max_score': 35933.757, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk35933757.jpg'}], 'start': 34739.422, 'title': 'Data science careers', 'summary': 'Discusses the increasing demand for data science skills, the importance of certifications on salary, key prerequisites for becoming a data scientist, essential skills including communication and teamwork, and explores the average salaries, demand, and differences in the field, emphasizing the lucrative nature of the career and the high demand for data scientists globally.', 'chapters': [{'end': 34972.532, 'start': 34739.422, 'title': 'Data science career insights', 'summary': 'Discusses the increasing demand for data science skills, the diminishing importance of degrees in hiring, and the significant impact of certifications on salary, emphasizing the potential for substantial pay increases and the importance of acquiring relevant skills and certifications.', 'duration': 233.11, 'highlights': ['Certifications can substantially increase pay, even doubling the salary, and are crucial for proving skills to potential employers. Obtaining relevant certifications can lead to a substantial increase in pay, potentially doubling the salary, and are essential for demonstrating skills to potential employers.', 'Big companies no longer prioritize degrees when hiring for data science roles, focusing more on skills and knowledge. Major companies have shifted their focus from degrees to skills and knowledge when hiring for data science roles, making degrees less important in the hiring process.', 'Data science offers lucrative career opportunities, including the potential for substantial pay increases and diverse job prospects. Data science presents lucrative career opportunities with the potential for substantial pay increases and diverse job prospects, making it an attractive field for individuals from various backgrounds.']}, {'end': 35239.638, 'start': 34972.572, 'title': 'Key prerequisites for data scientist', 'summary': "Discusses the key prerequisites for becoming a data scientist, including the importance of certifications, hands-on experience, and knowledge of machine learning, statistics, programming languages like python and r, and cloud computing technologies like aws. it emphasizes the significance of these prerequisites in today's world and their impact on solving real-world problems.", 'duration': 267.066, 'highlights': ['The importance of certifications and hands-on experience in cloud computing technologies like AWS for data scientists is emphasized, as it validates dedication and structured learning, while also showcasing practical knowledge to prospective employers and interviewers. Validation of dedication and structured learning, practical knowledge showcased to employers and interviewers', 'The significance of knowledge in machine learning, statistics, and programming languages like Python and R for data scientists is highlighted, as these form the foundation for understanding and implementing artificial intelligence and solving real-world problems. Foundation for understanding and implementing artificial intelligence, solving real-world problems', "The importance of cloud computing technologies like AWS and distributed computing tools like Apache Spark for data scientists is explained, as they enable scaling and handling of large volumes of data, which is crucial in today's data-driven world. Enabling scaling and handling of large volumes of data"]}, {'end': 35617.334, 'start': 35239.918, 'title': 'Essential skills for data scientists', 'summary': 'Emphasizes the importance of communication and teamwork skills for data scientists, along with the need for a focused area of expertise, knowledge of programming tools like python and r, and a strong foundation in statistics, machine learning, and data visualization.', 'duration': 377.416, 'highlights': ['Communication and teamwork skills are crucial for data scientists to work effectively with various teams across the company. The speaker emphasizes the need for good verbal and communication skills to collaborate with different teams, highlighting the importance of teamwork and the varied responsibilities of a data scientist.', 'Focused area of expertise is essential, and proficiency in programming tools like Python and R, along with a strong foundation in statistics, machine learning, and data visualization, is vital for a data scientist. The chapter stresses the importance of choosing a focused area of expertise and developing proficiency in programming tools like Python and R, as well as a strong foundation in statistics, machine learning, and data visualization.', 'Knowledge of big data fundamentals, cloud computing skills, and data warehousing is necessary for roles at companies like Spotify and Nike. The speaker discusses the requirements of data scientist roles at companies like Spotify and Nike, emphasizing the need for skills in big data fundamentals, cloud computing, and data warehousing for gathering, cleaning, and validating large amounts of data.']}, {'end': 36334.955, 'start': 35617.334, 'title': 'Data science: salaries, demand, and differences', 'summary': 'Explores the average salaries of data scientists in the usa, india, and the uk, highlighting the lucrative nature of the career, while also emphasizing the high demand for data scientists globally. it further delves into the differences between data science and data analytics, the companies hiring data scientists, and the essential skills required for both data scientists and data analysts.', 'duration': 717.621, 'highlights': ["Average Salaries of Data Scientists The average salary of a data scientist in the USA is over $132,000 per year, in India it's over 18 lakhs per annum, and in the UK it's 65,000 pounds a year, reflecting the lucrative nature of the career.", 'Demand for Data Scientists Numerous companies globally, including social media sites like Instagram, LinkedIn, and Facebook, are actively hiring data scientists on a daily basis, with a substantial demand-supply gap in the field.', 'Differences Between Data Science and Data Analytics Data science encompasses a wide scope and is focused on mining large datasets to drive meaningful insights, while data analytics is a more focused version and emphasizes utilizing existing data for visualizations and trend analysis.', 'Essential Skills for Data Scientists and Data Analysts Data scientists require strong knowledge of languages like Python, R, and Scala, as well as skills in handling unstructured data, back-end development, and machine learning. On the other hand, data analysts need statistical knowledge, programming skills, data wrangling abilities, and familiarity with big data technologies to perform prediction and visualization tasks.']}], 'duration': 1595.533, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk34739422.jpg', 'highlights': ['Certifications can substantially increase pay, potentially doubling the salary, and are crucial for proving skills to potential employers.', 'Major companies have shifted their focus from degrees to skills and knowledge when hiring for data science roles, making degrees less important in the hiring process.', 'Data science presents lucrative career opportunities with the potential for substantial pay increases and diverse job prospects, making it an attractive field for individuals from various backgrounds.', 'Validation of dedication and structured learning, practical knowledge showcased to employers and interviewers.', 'Foundation for understanding and implementing artificial intelligence, solving real-world problems.', 'Enabling scaling and handling of large volumes of data.', 'The speaker emphasizes the need for good verbal and communication skills to collaborate with different teams, highlighting the importance of teamwork and the varied responsibilities of a data scientist.', 'The chapter stresses the importance of choosing a focused area of expertise and developing proficiency in programming tools like Python and R, as well as a strong foundation in statistics, machine learning, and data visualization.', 'The speaker discusses the requirements of data scientist roles at companies like Spotify and Nike, emphasizing the need for skills in big data fundamentals, cloud computing, and data warehousing for gathering, cleaning, and validating large amounts of data.', "The average salary of a data scientist in the USA is over $132,000 per year, in India it's over 18 lakhs per annum, and in the UK it's 65,000 pounds a year, reflecting the lucrative nature of the career.", 'Numerous companies globally, including social media sites like Instagram, LinkedIn, and Facebook, are actively hiring data scientists on a daily basis, with a substantial demand-supply gap in the field.', 'Data science encompasses a wide scope and is focused on mining large datasets to drive meaningful insights, while data analytics is a more focused version and emphasizes utilizing existing data for visualizations and trend analysis.', 'Data scientists require strong knowledge of languages like Python, R, and Scala, as well as skills in handling unstructured data, back-end development, and machine learning.']}, {'end': 37793.955, 'segs': [{'end': 36373.662, 'src': 'embed', 'start': 36350.143, 'weight': 1, 'content': [{'end': 36363.895, 'text': 'So this time we are supposed to introduce 25% missing values in the iris dataset and impute the sepal length column with the mean and similarly impute the petal length column with the median.', 'start': 36350.143, 'duration': 13.752}, {'end': 36365.656, 'text': 'So this is the iris dataset.', 'start': 36364.235, 'duration': 1.421}, {'end': 36369.039, 'text': "So again, let's head back to RStudio and perform these tasks.", 'start': 36366.016, 'duration': 3.023}, {'end': 36371.621, 'text': 'Let me have a glance at the iris dataset first.', 'start': 36369.579, 'duration': 2.042}, {'end': 36373.662, 'text': 'View of iris.', 'start': 36371.921, 'duration': 1.741}], 'summary': 'Introduce 25% missing values in iris dataset, impute sepal length with mean, petal length with median, and perform tasks in rstudio.', 'duration': 23.519, 'max_score': 36350.143, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk36350143.jpg'}, {'end': 36500.268, 'src': 'embed', 'start': 36450.467, 'weight': 0, 'content': [{'end': 36460.11, 'text': "Now I'm supposed to impute this sepal length column with the mean and similarly this petal length column with the median,", 'start': 36450.467, 'duration': 9.643}, {'end': 36464.231, 'text': 'and to impute those two columns separately, I would require the HMISC package.', 'start': 36460.11, 'duration': 4.121}, {'end': 36467.712, 'text': 'So I load this package now library of HMISC.', 'start': 36464.891, 'duration': 2.821}, {'end': 36474.736, 'text': "And this package comprises of the impute function with the help of which I'll be imputing different columns.", 'start': 36469.272, 'duration': 5.464}, {'end': 36480.019, 'text': "So first I'll use the width function and this takes in two parameters.", 'start': 36475.256, 'duration': 4.763}, {'end': 36484.442, 'text': 'First is the data frame which consists of missing values.', 'start': 36480.319, 'duration': 4.123}, {'end': 36488.745, 'text': 'So in this data frame we are supposed to impute the missing values.', 'start': 36484.862, 'duration': 3.883}, {'end': 36492.886, 'text': 'So this is the iris.mis data frame and this is the first parameter.', 'start': 36489.045, 'duration': 3.841}, {'end': 36500.268, 'text': "Next we'll use the impute function and inside the impute function again it takes in two parameters.", 'start': 36493.386, 'duration': 6.882}], 'summary': 'Impute sepal length with mean and petal length with median using hmisc package', 'duration': 49.801, 'max_score': 36450.467, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk36450467.jpg'}, {'end': 36633.776, 'src': 'embed', 'start': 36611.091, 'weight': 5, 'content': [{'end': 36619.373, 'text': 'Well, linear regression is a supervised learning algorithm which helps us in finding the linear relationship between two variables.', 'start': 36611.091, 'duration': 8.282}, {'end': 36627.535, 'text': 'So one is the predictor or the independent variable and the other is the response or the dependent variable.', 'start': 36619.973, 'duration': 7.562}, {'end': 36633.776, 'text': 'So we try to understand how does the dependent variable change with the independent variable.', 'start': 36627.835, 'duration': 5.941}], 'summary': 'Linear regression is a supervised learning algorithm that finds the linear relationship between two variables.', 'duration': 22.685, 'max_score': 36611.091, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk36611091.jpg'}, {'end': 36909.911, 'src': 'embed', 'start': 36882.498, 'weight': 3, 'content': [{'end': 36886.88, 'text': 'And I will select all of these records and store them in the test set.', 'start': 36882.498, 'duration': 4.382}, {'end': 36889.602, 'text': 'So now we have our training and testing sets ready.', 'start': 36887.381, 'duration': 2.221}, {'end': 36892.904, 'text': "Now let's have a look at the number of rows in training and testing set.", 'start': 36890.102, 'duration': 2.802}, {'end': 36896.186, 'text': "So I'll type in and row of train.", 'start': 36893.444, 'duration': 2.742}, {'end': 36899.247, 'text': 'So see that there are 23 rows in the training set.', 'start': 36896.846, 'duration': 2.401}, {'end': 36902.408, 'text': "Similarly, I'll type in n row of test.", 'start': 36899.487, 'duration': 2.921}, {'end': 36905.769, 'text': 'And now we see that there are nine rows in the testing set.', 'start': 36902.948, 'duration': 2.821}, {'end': 36909.911, 'text': "Now we'll go ahead and build a model on top of the training set.", 'start': 36906.089, 'duration': 3.822}], 'summary': 'Data set divided into 23 rows for training and 9 rows for testing.', 'duration': 27.413, 'max_score': 36882.498, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk36882498.jpg'}, {'end': 37307.485, 'src': 'embed', 'start': 37279.215, 'weight': 2, 'content': [{'end': 37286.357, 'text': "So pd dot read underscore csv and the name of the file which is Boston dot csv and I'll store this in the data object.", 'start': 37279.215, 'duration': 7.142}, {'end': 37292.074, 'text': "so now that we've loaded the data, let me have a glance at the head of this data.", 'start': 37287.17, 'duration': 4.904}, {'end': 37295.957, 'text': 'so these are all the columns and the first five records present in the data frame.', 'start': 37292.074, 'duration': 3.883}, {'end': 37300.64, 'text': 'so we have crim, zn, indus, cas, nox, age and so on.', 'start': 37295.957, 'duration': 4.683}, {'end': 37307.485, 'text': 'and for the task of simple linear regression, medv is our dependent variable and lstat is our independent variable.', 'start': 37300.64, 'duration': 6.845}], 'summary': 'Data loaded from boston.csv, columns reviewed. medv as dependent, lstat as independent.', 'duration': 28.27, 'max_score': 37279.215, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk37279215.jpg'}, {'end': 37641.178, 'src': 'embed', 'start': 37611.381, 'weight': 7, 'content': [{'end': 37615.624, 'text': "So now that we've built the model, it's time to predict the values on top of the test set.", 'start': 37611.381, 'duration': 4.243}, {'end': 37621.191, 'text': "So again I'll use this instance and I will use the predict function with the help of this instance.", 'start': 37616.124, 'duration': 5.067}, {'end': 37630.884, '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': 37621.471, 'duration': 9.413}, {'end': 37635.551, 'text': "So we built the model and we've also predicted the values.", 'start': 37631.565, 'duration': 3.986}, {'end': 37641.178, 'text': 'Now let me have a glance at the number of rows and columns of the actual values and the predicted values.', 'start': 37636.051, 'duration': 5.127}], 'summary': 'Built model, predicted values, checked actual and predicted data dimensions.', 'duration': 29.797, 'max_score': 37611.381, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk37611381.jpg'}, {'end': 37769.303, 'src': 'embed', 'start': 37745.76, 'weight': 8, 'content': [{'end': 37754.904, 'text': 'so what we can see from this graph is so if virat kohli scores more than 50 runs, then there is a greater probability for team india to win the match.', 'start': 37745.76, 'duration': 9.144}, {'end': 37762.228, 'text': 'and similarly, if virat kohli scores less than 50 runs, then the probability of team india winning the match is less than 50 percent.', 'start': 37754.904, 'duration': 7.324}, {'end': 37764.441, 'text': "So let's take this value here.", 'start': 37763.12, 'duration': 1.321}, {'end': 37769.303, 'text': "So let's say the number of runs scored by Virat Kohli is around 60.", 'start': 37764.901, 'duration': 4.402}], 'summary': "Virat kohli scoring over 50 runs increases india's chance of winning.", 'duration': 23.543, 'max_score': 37745.76, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk37745760.jpg'}], 'start': 36335.595, 'title': 'Regression analysis in r and python', 'summary': 'Covers imputing missing values in the iris dataset, linear regression analysis in r, building linear models & calculating rmse, and linear regression & logistic regression, achieving a mean absolute error of 4.69, mean squared error of 43, and root mean squared error of 6.62, while emphasizing the importance of rmse in evaluating model performance.', 'chapters': [{'end': 36586.752, 'start': 36335.595, 'title': 'Imputing missing values in iris dataset', 'summary': 'Covers the introduction of 25% missing values in the iris dataset and the subsequent imputation of the sepal length column with the mean and the petal length column with the median using r packages, with the ideal cut carat range being highlighted.', 'duration': 251.157, 'highlights': ['The ideal cut carat range mentioned in the scatterplot starts from 0 and extends mostly till 3 or 4. This provides insights into the ideal cut carat range, which is important information for understanding diamond quality.', 'Introducing 25% missing values in the iris dataset using the Miss Forest package and then using the prod any function for introducing the missing values. Quantifiable data: Introducing 25% missing values into the iris dataset, demonstrating data manipulation techniques.', "Imputing the sepal length column with the mean and the petal length column with the median using the HMISC package's impute function. Quantifiable data: Imputing missing values in the sepal length and petal length columns, demonstrating data imputation techniques."]}, {'end': 36905.769, 'start': 36586.752, 'title': 'Linear regression analysis in r', 'summary': 'Discusses the concept of linear regression, its application in understanding the relationship between dependent and independent variables, and the process of dividing data into training and testing sets to avoid overfitting, exemplified through a demonstration in r with the empty cars data set, and the use of the carrot package for creating data partitions.', 'duration': 319.017, 'highlights': ['Linear regression is a supervised learning algorithm which helps us in finding the linear relationship between two variables. Linear regression is a supervised learning algorithm used to find the linear relationship between two variables, serving as a fundamental concept in data analysis.', 'In linear regression, there could be more than one independent variable, known as multiple linear regression. Linear regression can involve multiple independent variables, leading to multiple linear regression, expanding the scope of analysis beyond simple linear regression.', "The process of dividing a data set into training and testing sets is essential to prevent overfitting, ensuring the model's validity when applied to new data. Dividing data into training and testing sets is crucial to prevent overfitting, maintaining the model's accuracy and applicability to new data, emphasizing the significance of this step in the modeling process."]}, {'end': 37300.64, 'start': 36906.089, 'title': 'Building linear models & calculating rmse', 'summary': 'Discusses building a simple linear model on the training set to predict values on the test set, calculating rmse for the model, and implementing simple linear regression in python on the boston dataset, emphasizing the importance of rmse in evaluating model performance.', 'duration': 394.551, 'highlights': ['The RMSE value for the model built is 4.33, indicating the average error during prediction. The RMSE value for the model built on the training set is 4.33, providing an estimate of the average error during prediction.', 'Implementing simple linear regression in Python on the Boston dataset, emphasizing the importance of being proficient in both R and Python for data science interviews. The transcript emphasizes the necessity of being proficient in both R and Python for data science interviews and proceeds to implement simple linear regression in Python on the Boston dataset.', 'Predicted values of mileage for specific cars are provided, such as 22.55 for Mazda RX4 Wag and 20.04 for Valiant, after building the model on the train set and predicting values on the test set. The transcript provides specific predicted mileage values for various cars, such as 22.55 for Mazda RX4 Wag and 20.04 for Valiant, after building the model on the train set and predicting values on the test set.']}, {'end': 37793.955, 'start': 37300.64, 'title': 'Linear regression & logistic regression', 'summary': 'Covers the implementation of simple linear regression in python, including data understanding, model building, and performance evaluation, achieving a mean absolute error of 4.69, mean squared error of 43, and root mean squared error of 6.62. it also introduces logistic regression as a classification algorithm for binary dependent variables, illustrated with examples and the production of an s curve.', 'duration': 493.315, 'highlights': ["The mean absolute error for the linear regression model is 4.69, the mean squared error is 43, and the root mean squared error is 6.62, indicating the model's performance. Mean Absolute Error: 4.69, Mean Squared Error: 43, Root Mean Squared Error: 6.62", 'Logistic regression is introduced as a classification algorithm for binary dependent variables, with an example of predicting rain based on temperature and humidity. Logistic regression is used for binary dependent variables, illustrated with an example of predicting rain based on temperature and humidity.', 'The concept of logistic regression is explained through the production of an S curve, demonstrating the relationship between runs scored and the probability of winning a cricket match. Logistic regression is visualized with an S curve, showing the relationship between runs scored and the probability of winning a cricket match.']}], 'duration': 1458.36, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk36335595.jpg', 'highlights': ['Introducing 25% missing values into the iris dataset, demonstrating data manipulation techniques.', 'Imputing missing values in the sepal length and petal length columns, demonstrating data imputation techniques.', 'Linear regression is a supervised learning algorithm used to find the linear relationship between two variables, serving as a fundamental concept in data analysis.', 'Linear regression can involve multiple independent variables, leading to multiple linear regression, expanding the scope of analysis beyond simple linear regression.', "Dividing data into training and testing sets is crucial to prevent overfitting, maintaining the model's accuracy and applicability to new data, emphasizing the significance of this step in the modeling process.", 'The RMSE value for the model built on the training set is 4.33, providing an estimate of the average error during prediction.', "The mean absolute error for the linear regression model is 4.69, the mean squared error is 43, and the root mean squared error is 6.62, indicating the model's performance.", 'Logistic regression is used for binary dependent variables, illustrated with an example of predicting rain based on temperature and humidity.', 'Logistic regression is visualized with an S curve, showing the relationship between runs scored and the probability of winning a cricket match.']}, {'end': 40314.772, 'segs': [{'end': 37866.437, 'src': 'embed', 'start': 37837.441, 'weight': 6, 'content': [{'end': 37845.848, 'text': 'So this is our entire data frame and this target which, you see, this is our dependent variable and we have the age column over here,', 'start': 37837.441, 'duration': 8.407}, {'end': 37847.63, 'text': 'and this is the independent variable.', 'start': 37845.848, 'duration': 1.782}, {'end': 37849.851, 'text': 'So we are actually supposed to rename this.', 'start': 37848.15, 'duration': 1.701}, {'end': 37853.154, 'text': 'So we see that the column name is incorrect over here.', 'start': 37850.052, 'duration': 3.102}, {'end': 37856.317, 'text': 'So our first task could be to rename this column.', 'start': 37853.635, 'duration': 2.682}, {'end': 37858.278, 'text': 'Let me go ahead and do that.', 'start': 37856.537, 'duration': 1.741}, {'end': 37866.437, 'text': "So I'll use the column names function and I'll pass in this data frame which is heart and I am supposed to rename the first column.", 'start': 37859.295, 'duration': 7.142}], 'summary': 'Renaming the first column of the heart data frame.', 'duration': 28.996, 'max_score': 37837.441, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk37837441.jpg'}, {'end': 37919.436, 'src': 'embed', 'start': 37884.094, 'weight': 7, 'content': [{'end': 37886.235, 'text': 'Now let me have a structure of this data frame.', 'start': 37884.094, 'duration': 2.141}, {'end': 37888.356, 'text': 'So structure of heart.', 'start': 37886.575, 'duration': 1.781}, {'end': 37891.337, 'text': 'So we see that most of these are integers.', 'start': 37888.976, 'duration': 2.361}, {'end': 37899.74, 'text': 'But then again since we are building a logistic regression model on top of this data set this final target column is supposed to be categorical.', 'start': 37891.957, 'duration': 7.783}, {'end': 37901.161, 'text': 'It cannot be an integer.', 'start': 37899.9, 'duration': 1.261}, {'end': 37904.684, 'text': "So we'll go ahead and convert this into a factor.", 'start': 37901.701, 'duration': 2.983}, {'end': 37911.009, 'text': "So I'll use the as dot factor function and convert this integer value into a categorical value.", 'start': 37905.204, 'duration': 5.805}, {'end': 37919.436, 'text': "So as dot factor and I'll pass in this column over here which is heart dollar target and I'll store the result back to heart dollar target.", 'start': 37911.269, 'duration': 8.167}], 'summary': 'Converting integer target column to categorical using as.factor function.', 'duration': 35.342, 'max_score': 37884.094, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk37884094.jpg'}, {'end': 38016.86, 'src': 'embed', 'start': 37990.778, 'weight': 4, 'content': [{'end': 37996.043, 'text': 'Intellipaat provides a complete training on it and you can check out those details in the description box below.', 'start': 37990.778, 'duration': 5.265}, {'end': 37999.587, 'text': "And I'll store the result in log mod 1.", 'start': 37996.123, 'duration': 3.464}, {'end': 38003.131, 'text': "So now let me have a glance at the summary of the model which we've just built.", 'start': 37999.587, 'duration': 3.544}, {'end': 38006.673, 'text': 'So summary of log mod 1.', 'start': 38003.351, 'duration': 3.322}, {'end': 38013.157, 'text': 'So we have this P value over here and we see that there are three stars associated with this P value.', 'start': 38006.673, 'duration': 6.484}, {'end': 38016.86, 'text': 'So this basically means that we can reject the null hypothesis.', 'start': 38013.618, 'duration': 3.242}], 'summary': 'Intellipaat offers comprehensive training and discusses model summary, with a significant p value indicating rejection of null hypothesis.', 'duration': 26.082, 'max_score': 37990.778, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk37990778.jpg'}, {'end': 38286.061, 'src': 'embed', 'start': 38261.465, 'weight': 1, 'content': [{'end': 38270.331, 'text': "Now what I'll do is from the entire hard data frame, whichever records have the true label associated with them in the split tag.", 'start': 38261.465, 'duration': 8.866}, {'end': 38274.173, 'text': "I'll take all of those records and I'll store them in the training set.", 'start': 38270.331, 'duration': 3.842}, {'end': 38277.895, 'text': 'Similarly from the entire hard data frame.', 'start': 38274.613, 'duration': 3.282}, {'end': 38286.061, 'text': "wherever the split tag value is equal to false, I'll take all of them and I will store them in the test set.", 'start': 38277.895, 'duration': 8.166}], 'summary': 'Splitting hard data frame: true label in training set, false label in test set.', 'duration': 24.596, 'max_score': 38261.465, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk38261465.jpg'}, {'end': 38358.229, 'src': 'embed', 'start': 38332.08, 'weight': 8, 'content': [{'end': 38336.308, 'text': 'And since this is a logistic regression model, the family would be equal to binomial.', 'start': 38332.08, 'duration': 4.228}, {'end': 38340.1, 'text': "And I'll store this result in log mod 2.", 'start': 38336.709, 'duration': 3.391}, {'end': 38342.861, 'text': "Now that we've built the model, it's time to predict the values.", 'start': 38340.1, 'duration': 2.761}, {'end': 38345.343, 'text': "So I'll use the predict function.", 'start': 38343.642, 'duration': 1.701}, {'end': 38347.904, 'text': 'And this again takes in these three parameters.', 'start': 38345.683, 'duration': 2.221}, {'end': 38350.445, 'text': "First is the model which you've just built.", 'start': 38348.444, 'duration': 2.001}, {'end': 38354.827, 'text': 'And then second parameter is the object on which you want to predict the values.', 'start': 38351.105, 'duration': 3.722}, {'end': 38358.229, 'text': 'So we want to predict the values on top of the test set.', 'start': 38355.208, 'duration': 3.021}], 'summary': 'Logistic regression model built to predict values on test set.', 'duration': 26.149, 'max_score': 38332.08, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk38332080.jpg'}, {'end': 38398.126, 'src': 'embed', 'start': 38372.416, 'weight': 5, 'content': [{'end': 38380.74, 'text': 'So we see that the range of the predicted values of the heart disease, it varies from 0.21 to 0.86.', 'start': 38372.416, 'duration': 8.324}, {'end': 38385.322, 'text': 'That is the probability of the patient having heart disease.', 'start': 38380.74, 'duration': 4.582}, {'end': 38390.464, 'text': 'It varies from 21% to 86%.', 'start': 38385.742, 'duration': 4.722}, {'end': 38395.647, 'text': 'And this is how we can build a simple logistic regression model on top of this heart disease dataset.', 'start': 38390.464, 'duration': 5.183}, {'end': 38398.126, 'text': "Now let's head on to next question.", 'start': 38396.505, 'duration': 1.621}], 'summary': 'Logistic regression model predicts heart disease probability from 0.21 to 0.86.', 'duration': 25.71, 'max_score': 38372.416, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk38372416.jpg'}, {'end': 38454.965, 'src': 'embed', 'start': 38425.501, 'weight': 0, 'content': [{'end': 38427.422, 'text': 'So these denote all of the true positives.', 'start': 38425.501, 'duration': 1.921}, {'end': 38430.004, 'text': 'After that we have the false negatives.', 'start': 38427.963, 'duration': 2.041}, {'end': 38440.25, 'text': 'So false negatives denotes all of those records where the actual values were true, but the predicted value was false.', 'start': 38430.484, 'duration': 9.766}, {'end': 38445.974, 'text': 'So where the actual value is true, but the predicted value is false, that is known as a false negative.', 'start': 38440.751, 'duration': 5.223}, {'end': 38448.056, 'text': 'Then we have false positives.', 'start': 38446.514, 'duration': 1.542}, {'end': 38454.965, 'text': 'So in false positive the actual value is false but the predicted value is true.', 'start': 38448.677, 'duration': 6.288}], 'summary': 'The transcript discusses true positives, false negatives, and false positives in prediction.', 'duration': 29.464, 'max_score': 38425.501, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk38425501.jpg'}, {'end': 38615.913, 'src': 'embed', 'start': 38588.567, 'weight': 9, 'content': [{'end': 38595.588, 'text': 'So see that this is our confusion matrix and this left diagonal represents all of those values, which have been correctly classified.', 'start': 38588.567, 'duration': 7.021}, {'end': 38600.169, 'text': 'And this right diagonal represents all of those values, which have been incorrectly classified.', 'start': 38595.968, 'duration': 4.201}, {'end': 38605.87, 'text': 'And if you want to find the accuracy, all we have to do is divide this left diagonal with all of the values.', 'start': 38600.669, 'duration': 5.201}, {'end': 38615.913, 'text': "So that'll be 31 plus 17 divided by 31 plus 17 plus 10 plus 32.", 'start': 38606.43, 'duration': 9.483}], 'summary': 'The confusion matrix shows 31 true positives and 32 true negatives, with an accuracy of 0.63.', 'duration': 27.346, 'max_score': 38588.567, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk38588567.jpg'}], 'start': 37794.295, 'title': 'Implementing logistic and decision tree models', 'summary': 'Covers implementing logistic regression models on heart data and customer churn, achieving maximum probability of 82% for age of 29 and log loss value of 0.55, and building decision tree model on iris dataset with 96% accuracy and random forest model on ctg dataset with 96% accuracy.', 'chapters': [{'end': 38211.962, 'start': 37794.295, 'title': 'Implementing logistic regression on heart data', 'summary': 'Explains the process of implementing logistic regression on a heart dataset, renaming columns, converting integer values to categorical values, building a logistic regression model, and interpreting the probability of a person having heart disease based on their age, with a maximum probability of 82% for the age of 29 and a minimum probability of 26% for the age of 77.', 'duration': 417.667, 'highlights': ['Probability of a person not having heart disease is 82% at the age of 29 and 26% at the age of 77 The probability of a person not having heart disease decreases as age increases, with the highest probability at the age of 29 (82%) and the lowest probability at the age of 77 (26%).', "Renaming the 'IH' column to 'H' The 'IH' column in the dataset is successfully renamed to 'H' for clarity and consistency.", "P value with three stars indicating a strong relationship between 'H' column and the target column The P value with three stars suggests a strong relationship between the 'H' column (independent variable) and the target column (dependent variable) in the logistic regression model.", 'Deviance reduction from 417 to 401 after including age column in the model The inclusion of the age column in the model results in a reduction of deviance from 417 to 401, indicating a strong relationship between the age column and the target column.', 'Probability of a person not having heart disease at the age of 50 is 60% The probability of a person not having heart disease at the age of 50 is 60%, indicating a moderate likelihood of not having heart disease at this age.']}, {'end': 38915.879, 'start': 38212.242, 'title': 'Building logistic regression model in r', 'summary': 'Covers the process of splitting the data set into training and testing sets with a 70-30 split ratio, building a logistic regression model on the training set to predict the probability of heart disease based on age, assessing model accuracy, understanding true positive rate, false positive rate, and roc curve, and finally, building the roc curve to find the right trade-off between true positive and false positive rates with an accuracy of 53%.', 'duration': 703.637, 'highlights': ['The chapter covers the process of splitting the data set into training and testing sets with a 70-30 split ratio. The dataset is divided into training and testing sets with a split ratio of 70-30, resulting in 213 rows in the training set and 90 rows in the testing set.', 'Building a logistic regression model on the training set to predict the probability of heart disease based on age. A logistic regression model is built to predict the probability of heart disease based on age, resulting in a model accuracy of 53%.', 'Understanding true positive rate, false positive rate, and ROC curve to find the right trade-off between true positive and false positive rates. Explanation of true positive rate, false positive rate, and ROC curve as tools to assess model performance and find the right balance between true positive and false positive rates.']}, {'end': 39253.712, 'start': 38916.5, 'title': 'Logistic regression model for customer churn', 'summary': "Involves building a logistic regression model to predict customer churn based on monthly charges, splitting the data into training and testing sets, and evaluating the model's log loss value, which is found to be 0.55.", 'duration': 337.212, 'highlights': ['We split the data into training and testing sets with a test size of 30% and find the log loss value of the model to be 0.55. The data is divided into training and testing sets with 70% and 30% records respectively, and the log loss value of the model is calculated to be 0.55.', 'Explanation of decision tree algorithm for both classification and regression purposes. The decision tree algorithm is explained, highlighting its utility for both classification and regression tasks, and the structure of decision nodes and leaf nodes is described.', 'Description of logistic regression model fitting and prediction process, including importing the logistic regression function, fitting the model, and predicting probabilities on the test set. The process of fitting the logistic regression model and predicting probabilities on the test set is explained, including importing the logistic regression function, fitting the model, and storing the predicted probabilities.']}, {'end': 39817.723, 'start': 39254.172, 'title': 'Decision tree model and random forest working mechanism', 'summary': 'Explains the process of building a decision tree model on the iris dataset, achieving an accuracy of 96%, and then provides insights into the working mechanism of a random forest model, including the creation of multiple datasets and fitting decision trees with random subsets of columns.', 'duration': 563.551, 'highlights': ['The decision tree model achieves an accuracy of 96% on the iris dataset. The decision tree model built on the iris dataset achieves an accuracy of 96% through the process of training and testing, using the ctree function and creating a confusion matrix to determine the accuracy.', "Creation of multiple datasets from a single dataset for the random forest model. The working mechanism of the random forest model involves creating multiple datasets by drawing samples with replacement from a single dataset, enhancing the model's performance and diversity.", 'Fitting decision trees with random subsets of columns in the random forest model. In the random forest model, each decision tree is fitted using a random subset of columns at each node, promoting diversity and reducing overfitting in the model.']}, {'end': 40314.772, 'start': 39818.263, 'title': 'Working mechanism of random forest model', 'summary': 'Explains the working mechanism of random forest model, including the concept of random set of predictors, building the model on the ctg dataset, and achieving an accuracy of 96% in predicting cancer cases.', 'duration': 496.509, 'highlights': ['The chapter explains the concept of providing a random set of M predictors from the entire predictor space to make each tree in the random forest model very different from each other. Random set of M predictors, making trees in the random forest model very different', 'The speaker illustrates the process of building a random forest model on the CTG dataset, where NSP is the dependent variable and all other columns are independent variables. Building random forest model on the CTG dataset with NSP as the dependent variable', 'The model achieves an accuracy of 96% in predicting cancer cases based on the test set. Achieving 96% accuracy in predicting cancer cases']}], 'duration': 2520.477, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/osHjb7QhgWk/pics/osHjb7QhgWk37794295.jpg', 'highlights': ['Probability of a person not having heart disease is 82% at the age of 29 and 26% at the age of 77', 'Building decision tree model on iris dataset with 96% accuracy', 'Building random forest model on ctg dataset with 96% accuracy', 'The probability of a person not having heart disease decreases as age increases, with the highest probability at the age of 29 (82%) and the lowest probability at the age of 77 (26%)', "Renaming the 'IH' column to 'H'", "P value with three stars indicating a strong relationship between 'H' column and the target column", 'Deviance reduction from 417 to 401 after including age column in the model', 'The dataset is divided into training and testing sets with a split ratio of 70-30, resulting in 213 rows in the training set and 90 rows in the testing set', 'Understanding true positive rate, false positive rate, and ROC curve to find the right trade-off between true positive and false positive rates', 'The decision tree algorithm is explained, highlighting its utility for both classification and regression tasks, and the structure of decision nodes and leaf nodes is described', 'The decision tree model built on the iris dataset achieves an accuracy of 96%', 'Creation of multiple datasets from a single dataset for the random forest model', 'Fitting decision trees with random subsets of columns in the random forest model', 'The chapter explains the concept of providing a random set of M predictors from the entire predictor space to make each tree in the random forest model very different from each other', 'The model achieves an accuracy of 96% in predicting cancer cases based on the test set']}], 'highlights': ['The curriculum was affirmed by data science educators, providing learners with a well-structured and industry-relevant course for pursuing a career in data science.', 'The course was designed based on an analysis of resumes and job postings related to data science, with skill sets confirmed by hiring managers and data science educators, ensuring its relevance to the industry.', 'Introduction to the session and the purpose of the data science course, addressing common doubts and questions about pursuing a career in data science.', 'Importance of coding skills for data scientists, with the distinction between the level of coding expertise required for a data scientist compared to a software development engineer.', "Examples of data science impact, such as Microsoft's accurate Oscar predictions and AI-authored books, showcasing the practical applications and advancements in the field.", 'In-depth explanation of the day-to-day work of a data scientist, including the ability to formulate questions and find answers within provided data sets.', 'Data science offers sustainable career opportunities, with increasing demand for professionals as more companies adopt data science. The speaker emphasizes that the data science field is experiencing significant growth, with increasing adoption by companies, leading to a growing demand for data science professionals, making it an opportune time to start a career in this domain.', "The app 'fake faces' uses GAN algorithm to generate indistinguishable fake faces from a database of millions of human faces.", 'Data science implementation in a finance company resulted in higher conversion rates and millions in savings from reduced manual efforts.', "Amazon Alexa's versatility enables it to control smart home devices, order from online services, and play music, with integration capabilities extending to services like Uber and Domino's.", "Netflix's recommendation system influences the majority of content choices, leveraging data from 250 million active profiles and combining user behavior data with content tags.", 'Reinforcement learning in self-driving cars has potential to significantly reduce road accidents, with a reported 90% decrease attributed to human error.', 'Linear regression helps understand the relationship between variables and model performance through the interpretation of R-squared value, with a low R-squared indicating considerable error.', 'Logistic regression is used for classification problems where the data is skewed and can be categorized into two distinct values, such as 0 and 1, with typical examples including tumor prediction, spam classification, and fraudulent transaction detection.', 'The distinction between linear regression and logistic regression is outlined, highlighting their use cases based on the nature of variables and the type of output.', 'The importance of understanding the nature of variables, such as continuous and categorical, is emphasized in determining the appropriate regression technique for a given data set.', 'Support Vector Machine achieves the highest accuracy of 0.99, indicating its potential as the best model for prediction.', 'The library has been created by expert data scientists, is well-tested and open-source, allowing developers to contribute patches, bug fixes, and new features.', 'Data Splitting for Model Training and Testing Emphasizing the process of splitting the data into training and testing sets to train the model and assess its accuracy, with a recommended 70-30 split for small datasets and the testing set used for model performance validation.', 'NumPy is the most widely used Python library for linear algebra, facilitating mathematical and logical operations on multi-dimensional arrays.', 'SciPy provides modules for optimization, linear algebra, integration, interpolation, and more, offering efficient tools for scientific and technical computing.', 'Pandas is suitable for tabular, time series, and arbitrary matrix data, expanding its applicability to diverse data structures.', 'The chapter covers how to calculate mean, median, standard deviation, maximum and minimum values, count, and descriptive statistics summary of a data frame It explains how to calculate the mean, median, standard deviation, maximum and minimum values, count, and descriptive statistics summary of a data frame.', 'The chapter stresses the importance of choosing a focused area of expertise and developing proficiency in programming tools like Python and R, as well as a strong foundation in statistics, machine learning, and data visualization.', 'Certifications can substantially increase pay, potentially doubling the salary, and are crucial for proving skills to potential employers.', "The average salary of a data scientist in the USA is over $132,000 per year, in India it's over 18 lakhs per annum, and in the UK it's 65,000 pounds a year, reflecting the lucrative nature of the career."]}