title

Statistics And Probability Tutorial | Statistics And Probability for Data Science | Edureka

description

π₯ Data Science Certification using R (Use Code "πππππππππ"): https://www.edureka.co/data-science
This session on Statistics And Probability will cover all the fundamentals of stats and probability along with a practical demonstration in the R language. The following topics are covered in this session:
3:23 What Is Data?
4:17 Categories Of Data
9:01 What Is Statistics?
11:20 Basic Terminologies In Statistics
12:35 Sampling Techniques
17:46 Types Of Statistics
20:22 Descriptive Statistics
21:25 Measures Of Centre
25:40 Measures Of Spread
32:06 Information Gain & Entropy
44:13 Confusion Matrix
49:00 Descriptive Statistics Demo
53:09 Probability
55:33 Terminologies In Probability
57:46 Probability Distribution
1:03:00 Types Of Probability
1:10:00 Bayes' Theorem
1:15:34 Inferential Statistics
1:16:09 Point Estimation
1:19:05 Interval Estimation
1:22:23 Margin Of Error
1:22:57 Estimating Level Of Confidence
1:26:25 Hypothesis Testing
1:30:25 Inferential Statistics Demo
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
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About the Course
Edureka's Data Science course will cover the whole data lifecycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
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Why Learn Data Science?
Data Science training certifies you with βin demandβ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the Data Science course, you should be able to:
1. Gain insight into the 'Roles' played by a Data Scientist
2. Analyze Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyze data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
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Who should go for this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies.
For online Data Science training, please write back to us at sales@edureka.in or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

detail

{'title': 'Statistics And Probability Tutorial | Statistics And Probability for Data Science | Edureka', 'heatmap': [{'end': 1863.663, 'start': 1742.256, 'weight': 1}], 'summary': "This tutorial on statistics and probability for data science emphasizes their crucial role, covering topics such as data types, descriptive statistics, probability distributions, measures of central tendency and spread, decision tree analysis, r programming, probability types, and bayes' theorem with practical use cases and examples.", 'chapters': [{'end': 194.008, 'segs': [{'end': 62.347, 'src': 'embed', 'start': 11.903, 'weight': 0, 'content': [{'end': 21.71, 'text': 'Statistics and probability are essential because these disciples form the basic foundation of all machine learning algorithms, deep learning,', 'start': 11.903, 'duration': 9.807}, {'end': 24.331, 'text': 'artificial intelligence and data science.', 'start': 21.71, 'duration': 2.621}, {'end': 28.935, 'text': 'In fact, mathematics and probability is behind everything around us.', 'start': 24.932, 'duration': 4.003}, {'end': 37.701, 'text': 'From shapes, patterns, and colors to the count of petals in a flower, mathematics is embedded in each and every aspect of our lives.', 'start': 29.415, 'duration': 8.286}, {'end': 41.063, 'text': "With this in mind, I welcome you all to today's session.", 'start': 38.121, 'duration': 2.942}, {'end': 47.504, 'text': "Hi everyone, I'm Zulekha from Edureka and this session is all about statistics and probability.", 'start': 41.643, 'duration': 5.861}, {'end': 51.765, 'text': "So I'm gonna go ahead and discuss the agenda for today with you all.", 'start': 48.264, 'duration': 3.501}, {'end': 56.266, 'text': "We're gonna begin this session by understanding what is data.", 'start': 51.785, 'duration': 4.481}, {'end': 62.347, 'text': "After that, we'll move on and look at the different categories of data like quantitative and qualitative data.", 'start': 56.906, 'duration': 5.441}], 'summary': 'Statistics and probability are fundamental to machine learning, ai, and data science, underpinning everything in our lives.', 'duration': 50.444, 'max_score': 11.903, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo11903.jpg'}, {'end': 194.008, 'src': 'embed', 'start': 148.782, 'weight': 2, 'content': [{'end': 152.705, 'text': "I'll leave a couple of blogs and a couple of videos in the description box.", 'start': 148.782, 'duration': 3.923}, {'end': 154.846, 'text': "Y'all can definitely check out that content.", 'start': 152.845, 'duration': 2.001}, {'end': 161.085, 'text': "Now after we've completed the probability module, we'll discuss the inferential statistics module.", 'start': 155.62, 'duration': 5.465}, {'end': 165.069, 'text': "We'll start this module by understanding what is point estimation.", 'start': 161.486, 'duration': 3.583}, {'end': 171.014, 'text': "We'll discuss what is confidence interval and how you can estimate the confidence interval.", 'start': 165.629, 'duration': 5.385}, {'end': 177.601, 'text': "We'll also discuss margin of error and we'll understand all of these concepts by looking at a small use case.", 'start': 171.455, 'duration': 6.146}, {'end': 184.044, 'text': "We'll finally end the inferential statistic module by looking at what hypothesis testing is.", 'start': 178.181, 'duration': 5.863}, {'end': 188.526, 'text': 'Hypothesis testing is a very important part of inferential statistics,', 'start': 184.504, 'duration': 4.022}, {'end': 194.008, 'text': "so we'll end the session by looking at a use case that discusses how hypothesis testing works.", 'start': 188.526, 'duration': 5.482}], 'summary': 'Upcoming content includes probability and inferential statistics modules, covering point estimation, confidence intervals, margin of error, and hypothesis testing.', 'duration': 45.226, 'max_score': 148.782, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo148782.jpg'}], 'start': 11.903, 'title': 'The role of statistics and probability in machine learning', 'summary': 'Emphasizes the crucial role of statistics and probability in machine learning, artificial intelligence, and data science, covering key topics such as data types, descriptive statistics, probability distributions, bayes theorem, inferential statistics, and practical use cases.', 'chapters': [{'end': 127.504, 'start': 11.903, 'title': 'Importance of statistics and probability in machine learning', 'summary': 'Emphasizes the crucial role of statistics and probability in machine learning, artificial intelligence, and data science, highlighting the relevance of mathematics in various aspects of daily life. it also outlines the agenda for the session, covering key topics such as data types, descriptive statistics, probability distributions, and practical use cases.', 'duration': 115.601, 'highlights': ['The chapter emphasizes the crucial role of statistics and probability in machine learning, artificial intelligence, and data science. Statistics and probability are essential for machine learning, artificial intelligence, and data science, forming the basic foundation for all related algorithms.', 'The chapter outlines the agenda for the session, covering key topics such as data types, descriptive statistics, probability distributions, and practical use cases. The session agenda includes understanding data types, descriptive statistics, probability distributions, and practical use cases to demonstrate the application of statistics and probability concepts.', 'Mathematics is embedded in each and every aspect of our lives, from shapes, patterns, and colors to the count of petals in a flower. Mathematics is ingrained in various aspects of daily life, including shapes, patterns, colors, and natural phenomena, showcasing its pervasive influence.']}, {'end': 194.008, 'start': 128.386, 'title': 'Understanding bayes theorem and inferential statistics', 'summary': 'Covers the practical application of bayes theorem and provides an overview of the inferential statistics module, including point estimation, confidence interval, margin of error, and hypothesis testing.', 'duration': 65.622, 'highlights': ['The chapter covers the practical application of Bayes Theorem', 'The module will discuss the inferential statistics module, including point estimation, confidence interval, margin of error, and hypothesis testing', 'The session will end by looking at a use case that discusses how hypothesis testing works', 'After completing the probability module, the session will move on to the inferential statistics module']}], 'duration': 182.105, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo11903.jpg', 'highlights': ['Statistics and probability are essential for machine learning, artificial intelligence, and data science, forming the basic foundation for all related algorithms.', 'The chapter outlines the agenda for the session, covering key topics such as data types, descriptive statistics, probability distributions, and practical use cases.', 'The module will discuss the inferential statistics module, including point estimation, confidence interval, margin of error, and hypothesis testing.', 'The session will end by looking at a use case that discusses how hypothesis testing works.', 'After completing the probability module, the session will move on to the inferential statistics module.']}, {'end': 819.407, 'segs': [{'end': 242.811, 'src': 'embed', 'start': 217.066, 'weight': 0, 'content': [{'end': 221.59, 'text': 'Now data is actually everything, all right? Look around you, there is data everywhere.', 'start': 217.066, 'duration': 4.524}, {'end': 225.414, 'text': 'Each click on your phone generates more data than you know.', 'start': 222.171, 'duration': 3.243}, {'end': 232.059, 'text': 'Now this generated data provides insights for analysis and helps us make better business decisions.', 'start': 225.894, 'duration': 6.165}, {'end': 234.722, 'text': 'This is why data is so important.', 'start': 232.68, 'duration': 2.042}, {'end': 242.811, 'text': 'To give you a formal definition, data refers to facts and statistics collected together for reference or analysis.', 'start': 235.362, 'duration': 7.449}], 'summary': 'Data is abundant, with each phone click generating valuable insights for analysis and business decisions.', 'duration': 25.745, 'max_score': 217.066, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo217066.jpg'}, {'end': 292.937, 'src': 'embed', 'start': 269.07, 'weight': 1, 'content': [{'end': 276.398, 'text': 'Under qualitative data we have nominal and ordinal data and under quantitative data we have discrete and continuous data.', 'start': 269.07, 'duration': 7.328}, {'end': 279.511, 'text': "Now let's focus on qualitative data.", 'start': 277.23, 'duration': 2.281}, {'end': 287.915, 'text': "Now this type of data deals with characteristics and descriptors that can't be easily measured but can be observed subjectively.", 'start': 279.851, 'duration': 8.064}, {'end': 292.937, 'text': 'Now qualitative data is further divided into nominal and ordinal data.', 'start': 288.515, 'duration': 4.422}], 'summary': "Qualitative data includes nominal and ordinal data, and deals with characteristics and descriptors that can't be easily measured.", 'duration': 23.867, 'max_score': 269.07, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo269070.jpg'}, {'end': 396.777, 'src': 'embed', 'start': 369.554, 'weight': 3, 'content': [{'end': 373.317, 'text': 'it deals with anything that you can measure objectively, all right?', 'start': 369.554, 'duration': 3.763}, {'end': 376.539, 'text': 'So there are two types of quantitative data.', 'start': 374.017, 'duration': 2.522}, {'end': 378.941, 'text': 'There is discrete and continuous data.', 'start': 376.779, 'duration': 2.162}, {'end': 386.652, 'text': 'Now discrete data is also known as categorical data and it can hold a finite number of possible values.', 'start': 379.548, 'duration': 7.104}, {'end': 390.453, 'text': 'Now the number of students in a class is a finite number.', 'start': 387.312, 'duration': 3.141}, {'end': 393.615, 'text': "Alright, you can't have infinite number of students in a class.", 'start': 390.473, 'duration': 3.142}, {'end': 396.777, 'text': "Let's say in your fifth grade there were 100 students in your class.", 'start': 394.155, 'duration': 2.622}], 'summary': 'Quantitative data can be discrete or continuous, with finite values like 100 students in a class.', 'duration': 27.223, 'max_score': 369.554, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo369554.jpg'}, {'end': 500.548, 'src': 'embed', 'start': 475.412, 'weight': 4, 'content': [{'end': 484.317, 'text': "That's when you call a variable as discrete or categorical variable because it can hold values that represent different categories of data.", 'start': 475.412, 'duration': 8.905}, {'end': 490.598, 'text': 'Now continuous variables are basically variables that can store infinite number of values.', 'start': 485.032, 'duration': 5.566}, {'end': 494.562, 'text': 'So the weight of a person can be denoted as a continuous variable.', 'start': 491.158, 'duration': 3.404}, {'end': 500.548, 'text': "Let's say there is a variable called weight and it can store infinite number of possible values.", 'start': 495.182, 'duration': 5.366}], 'summary': 'Categorical variables can represent different categories of data, while continuous variables can hold infinite values, such as weight.', 'duration': 25.136, 'max_score': 475.412, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo475412.jpg'}, {'end': 566.369, 'src': 'embed', 'start': 542.62, 'weight': 2, 'content': [{'end': 553.064, 'text': 'Now coming to the formal definition of statistics, statistics is an area of applied mathematics which is concerned with data collection, analysis,', 'start': 542.62, 'duration': 10.444}, {'end': 554.885, 'text': 'interpretation and presentation.', 'start': 553.064, 'duration': 1.821}, {'end': 560.487, 'text': 'Now usually when I speak about statistics, people think statistics is all about analysis.', 'start': 555.285, 'duration': 5.202}, {'end': 563.788, 'text': 'but statistics has other parts to it.', 'start': 561.326, 'duration': 2.462}, {'end': 566.369, 'text': 'It has data collection is also a part of statistics.', 'start': 563.888, 'duration': 2.481}], 'summary': 'Statistics is an area of applied mathematics focused on data collection, analysis, interpretation, and presentation.', 'duration': 23.749, 'max_score': 542.62, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo542620.jpg'}, {'end': 652.866, 'src': 'embed', 'start': 591.562, 'weight': 6, 'content': [{'end': 596.827, 'text': "okay, let's say that your company has created a new drug that may cure cancer.", 'start': 591.562, 'duration': 5.265}, {'end': 601.471, 'text': "how would you conduct a test to confirm the drug's effectiveness?", 'start': 596.827, 'duration': 4.644}, {'end': 606.256, 'text': 'now, even though this sounds like a biology problem, this can be solved with statistics.', 'start': 601.471, 'duration': 4.785}, {'end': 610.6, 'text': 'all right, you will have to create a test which can confirm the effectiveness of the drug.', 'start': 606.256, 'duration': 4.344}, {'end': 614.663, 'text': 'all right, this is a common problem that can be solved using statistics.', 'start': 610.6, 'duration': 4.063}, {'end': 616.065, 'text': 'let me give you another example.', 'start': 614.663, 'duration': 1.402}, {'end': 625.181, 'text': 'You and a friend are at a baseball game and, out of the blue, he offers you a bet that neither team will hit a home run in that game.', 'start': 616.835, 'duration': 8.346}, {'end': 631.205, 'text': "Should you take the bet? All right, here you just discuss the probability of whether you'll win or lose.", 'start': 625.962, 'duration': 5.243}, {'end': 634.448, 'text': 'All right, this is another problem that comes under statistics.', 'start': 631.546, 'duration': 2.902}, {'end': 636.749, 'text': "Let's look at another example.", 'start': 635.268, 'duration': 1.481}, {'end': 645.756, 'text': 'The latest sales data has just come in and your boss wants you to prepare a report for management on places where the company could improve its business.', 'start': 637.55, 'duration': 8.206}, {'end': 652.866, 'text': 'What should you look for and what should you not look for? Now this problem involves a lot of data analysis.', 'start': 646.589, 'duration': 6.277}], 'summary': 'Using statistics to test drug effectiveness, predict bet outcomes, and analyze sales data for improvement.', 'duration': 61.304, 'max_score': 591.562, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo591562.jpg'}, {'end': 709.442, 'src': 'embed', 'start': 686.187, 'weight': 5, 'content': [{'end': 693.252, 'text': 'Now before you dive deep into statistics, it is important that you understand basic terminologies used in statistics.', 'start': 686.187, 'duration': 7.065}, {'end': 699.356, 'text': 'The two most important terminologies in statistics are population and sample.', 'start': 693.832, 'duration': 5.524}, {'end': 707.821, 'text': "So, throughout the statistics course or throughout any problem that you're trying to solve with statistics, you will come across these two words,", 'start': 699.856, 'duration': 7.965}, {'end': 709.442, 'text': 'which is population and sample.', 'start': 707.821, 'duration': 1.621}], 'summary': 'Understanding basic statistics terminologies: population and sample.', 'duration': 23.255, 'max_score': 686.187, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo686187.jpg'}, {'end': 782.081, 'src': 'embed', 'start': 757.398, 'weight': 9, 'content': [{'end': 763.944, 'text': 'Now sampling is a statistical method that deals with the selection of individual observations within a population.', 'start': 757.398, 'duration': 6.546}, {'end': 770.01, 'text': 'So sampling is performed in order to infer statistical knowledge about a population.', 'start': 764.545, 'duration': 5.465}, {'end': 775.875, 'text': 'If you want to understand the different statistics of a population, like the mean, the median,', 'start': 770.63, 'duration': 5.245}, {'end': 782.081, 'text': "the mode or the standard deviation or the variance of a population, then you're going to perform sampling.", 'start': 775.875, 'duration': 6.206}], 'summary': "Sampling is a statistical method to infer knowledge about a population's statistics like mean, median, mode, standard deviation, and variance.", 'duration': 24.683, 'max_score': 757.398, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo757398.jpg'}], 'start': 194.689, 'title': 'Understanding data types and statistics application', 'summary': 'Provides an overview of data types in statistics, including qualitative and quantitative data, and their subcategories. it also discusses the application of statistics in real-life scenarios, such as drug effectiveness testing and sales data analysis.', 'chapters': [{'end': 344.452, 'start': 194.689, 'title': 'Understanding data types in statistics', 'summary': 'Provides an overview of data in statistics, highlighting its significance in analysis and decision-making. it explains the types of data including qualitative and quantitative data, and further delves into subcategories such as nominal and ordinal data.', 'duration': 149.763, 'highlights': ['Data is everything and is vital for analysis and decision-making, with each click on a phone generating substantial data. The generated data provides insights for analysis and helps make better business decisions, emphasizing the importance of data in various contexts.', "Qualitative data encompasses characteristics and descriptors that can't be easily measured but can be observed subjectively, including nominal and ordinal data. Qualitative data deals with characteristics and descriptors that can't be easily measured but can be observed subjectively, further divided into nominal and ordinal data.", 'Nominal data lacks order or ranking, exemplified by gender and race, while ordinal data involves an ordered series of information, such as customer ratings for a restaurant service. Nominal data includes examples like gender and race, while ordinal data involves an ordered series of information, demonstrated through customer ratings for a restaurant service.']}, {'end': 590.884, 'start': 345.513, 'title': 'Understanding ordinal and quantitative data', 'summary': 'Explains ordinal data, discrete and continuous quantitative data, and the types of variables, along with an introduction to statistics as an area of applied mathematics.', 'duration': 245.371, 'highlights': ['Statistics is an area of applied mathematics concerned with data collection, analysis, interpretation, and presentation. Statistics encompasses data collection, analysis, interpretation, and presentation, providing a comprehensive approach to understanding data.', 'Quantitative data includes discrete data, which holds a finite number of possible values, and continuous data, which can hold an infinite number of possible values. Quantitative data comprises discrete (or categorical) data with finite values and continuous data with infinite possible values, such as the number of students in a class and the weight of a person, respectively.', 'Variables encompass discrete (categorical) and continuous variables, with the former holding values of different categories and the latter storing an infinite number of values. Variables include discrete (or categorical) variables with different category values and continuous variables capable of storing an infinite range of values, exemplified by the weight of a person.']}, {'end': 819.407, 'start': 591.562, 'title': 'Statistics in real life', 'summary': 'Discusses the application of statistics in various real-life scenarios, including testing the effectiveness of a new drug, evaluating the probability of a bet, analyzing sales data, and understanding basic terminologies in statistics, such as population and sample.', 'duration': 227.845, 'highlights': ['Understanding the basic terminologies in statistics, including population and sample, is crucial for analyzing real-world data. The chapter emphasizes the importance of understanding basic terminologies in statistics, such as population and sample, for analyzing real-world data.', 'Applying statistical methods to analyze sales data can help identify areas for business improvement and growth. The chapter illustrates how statistical techniques can be used to analyze sales data and identify areas for business improvement and growth.', 'Applying statistical methods to test the effectiveness of a new drug is essential for confirming its potential to cure cancer. The chapter emphasizes the use of statistical methods to conduct tests and confirm the effectiveness of a new drug, potentially benefiting cancer treatment.', 'Evaluating the probability of a bet using statistics provides a rational approach to decision-making in real-life scenarios. The chapter highlights the application of statistics in evaluating the probability of a bet, offering a rational approach to decision-making in real-life scenarios.', 'Sampling is a statistical method used to infer knowledge about a population, making it essential for practical data analysis. The importance of sampling as a statistical method for inferring knowledge about a population is emphasized, particularly for practical data analysis.']}], 'duration': 624.718, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo194689.jpg', 'highlights': ['The importance of data in various contexts, emphasizing its vital role in analysis and decision-making.', "Qualitative data encompasses characteristics and descriptors that can't be easily measured but can be observed subjectively, including nominal and ordinal data.", 'Statistics encompasses data collection, analysis, interpretation, and presentation, providing a comprehensive approach to understanding data.', 'Quantitative data comprises discrete (or categorical) data with finite values and continuous data with infinite possible values, such as the number of students in a class and the weight of a person, respectively.', 'Variables include discrete (or categorical) variables with different category values and continuous variables capable of storing an infinite range of values.', 'The chapter emphasizes the importance of understanding basic terminologies in statistics, such as population and sample, for analyzing real-world data.', 'The chapter illustrates how statistical techniques can be used to analyze sales data and identify areas for business improvement and growth.', 'The chapter emphasizes the use of statistical methods to conduct tests and confirm the effectiveness of a new drug, potentially benefiting cancer treatment.', 'The chapter highlights the application of statistics in evaluating the probability of a bet, offering a rational approach to decision-making in real-life scenarios.', 'The importance of sampling as a statistical method for inferring knowledge about a population is emphasized, particularly for practical data analysis.']}, {'end': 1524.142, 'segs': [{'end': 1068.007, 'src': 'embed', 'start': 1044.34, 'weight': 0, 'content': [{'end': 1052.765, 'text': 'So random sampling, meaning that all of the individuals in each of the stratum will have an equal chance of being selected in the sample correct?', 'start': 1044.34, 'duration': 8.425}, {'end': 1056.407, 'text': 'So, guys, these were the three different types of sampling techniques.', 'start': 1053.145, 'duration': 3.262}, {'end': 1062.57, 'text': "Now let's move on and look at our next topic which is the different types of statistics.", 'start': 1057.147, 'duration': 5.423}, {'end': 1068.007, 'text': "So after this we'll be looking at the more advanced concepts of statistics.", 'start': 1063.405, 'duration': 4.602}], 'summary': 'Overview of three sampling techniques and upcoming topics in statistics.', 'duration': 23.667, 'max_score': 1044.34, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo1044339.jpg'}, {'end': 1197.876, 'src': 'embed', 'start': 1170.313, 'weight': 1, 'content': [{'end': 1178.136, 'text': 'Okay, so in simple words, it generalizes a large data set and it applies probability to draw a conclusion.', 'start': 1170.313, 'duration': 7.823}, {'end': 1184.959, 'text': 'Okay, so it allows you to infer data parameters based on a statistical model by using sample data.', 'start': 1178.516, 'duration': 6.443}, {'end': 1192.414, 'text': 'So, if we consider the same example of finding the average shirt size of students in a class in inferential statistics,', 'start': 1185.572, 'duration': 6.842}, {'end': 1197.876, 'text': 'you will take a sample set of the class, which is basically a few people from the entire class.', 'start': 1192.414, 'duration': 5.462}], 'summary': 'Inferential statistics generalizes large data sets and uses probability to draw conclusions based on sample data.', 'duration': 27.563, 'max_score': 1170.313, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo1170313.jpg'}, {'end': 1253.517, 'src': 'embed', 'start': 1229.428, 'weight': 2, 'content': [{'end': 1240.212, 'text': 'descriptive statistics is a method that is used to describe and understand the features of a specific data set by giving short summaries about the sample and measures of the data.', 'start': 1229.428, 'duration': 10.784}, {'end': 1243.873, 'text': 'There are two important measures in descriptive statistics.', 'start': 1240.772, 'duration': 3.101}, {'end': 1251.196, 'text': 'We have measure of central tendency, which is also known as measure of center, and we have measures of variability.', 'start': 1244.153, 'duration': 7.043}, {'end': 1253.517, 'text': 'This is also known as measures of spread.', 'start': 1251.576, 'duration': 1.941}], 'summary': 'Descriptive statistics summarizes data features and measures of central tendency and variability.', 'duration': 24.089, 'max_score': 1229.428, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo1229428.jpg'}], 'start': 820.658, 'title': 'Sampling and probability techniques', 'summary': 'Covers sampling as a shortcut for statistical analysis, emphasizing probability sampling methods such as random, systematic, and stratified sampling, while introducing descriptive and inferential statistics, including measures of central tendency and variability.', 'chapters': [{'end': 865.749, 'start': 820.658, 'title': 'Sampling as a shortcut', 'summary': 'Explains the concept of sampling as a method to draw inference about the entire population by studying a sample, saving time and resources in statistical analysis.', 'duration': 45.091, 'highlights': ['Sampling is a method to draw inference about the entire population by studying a sample, saving time and resources in statistical analysis.', "It's a shortcut to studying the entire population, representing it with a small sample and performing statistical analysis on that sample."]}, {'end': 1524.142, 'start': 866.87, 'title': 'Probability sampling techniques & descriptive statistics', 'summary': 'Covers the principles of probability sampling, including types such as random, systematic, and stratified sampling, and introduces descriptive and inferential statistics, explaining the measures of central tendency and variability.', 'duration': 657.272, 'highlights': ['Probability sampling involves three main types: random, systematic, and stratified sampling, with each method ensuring equal chances of selection from the population. Probability sampling techniques include random, systematic, and stratified sampling, ensuring equal chances of selection from the population, as illustrated by examples such as systematic sampling choosing every nth record from the population.', 'Descriptive statistics focuses on describing and understanding features of a specific dataset, using measures of central tendency including mean, median, and mode, and measures of variability such as range, interquartile range, variance, and standard deviation. Descriptive statistics involves measures of central tendency (mean, median, mode) and measures of variability (range, interquartile range, variance, standard deviation) to summarize and understand specific data sets, exemplified by finding the average horsepower and median mileage per gallon in a car dataset.', 'Inferential statistics makes predictions about a population based on sample data, allowing inference of data parameters using statistical models and sample data to generalize findings for the entire population. Inferential statistics involves making predictions about a population based on sample data, using statistical models and sample data to infer data parameters and generalize findings, demonstrated through the example of predicting the average shirt size of students in a class.']}], 'duration': 703.484, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo820658.jpg', 'highlights': ['Probability sampling techniques include random, systematic, and stratified sampling, ensuring equal chances of selection from the population.', 'Inferential statistics involves making predictions about a population based on sample data, using statistical models and sample data to infer data parameters and generalize findings.', 'Descriptive statistics involves measures of central tendency (mean, median, mode) and measures of variability (range, interquartile range, variance, standard deviation) to summarize and understand specific data sets.']}, {'end': 1920.141, 'segs': [{'end': 1556.071, 'src': 'embed', 'start': 1524.723, 'weight': 0, 'content': [{'end': 1529.948, 'text': 'So basically we have three four-type cylinders and we have five six-type cylinders.', 'start': 1524.723, 'duration': 5.225}, {'end': 1535.153, 'text': 'So our mode is going to be six since six is more recurrent than four.', 'start': 1530.889, 'duration': 4.264}, {'end': 1540.14, 'text': 'So guys, those were the measures of the center or the measures of central tendency.', 'start': 1535.797, 'duration': 4.343}, {'end': 1543.943, 'text': "Now let's move on and look at the measures of the spread.", 'start': 1540.74, 'duration': 3.203}, {'end': 1547.145, 'text': 'Now, what is the measure of spread?', 'start': 1543.963, 'duration': 3.182}, {'end': 1556.071, 'text': 'A measure of spread, sometimes also called as measure of dispersion, is used to describe the variability in a sample or population.', 'start': 1547.545, 'duration': 8.526}], 'summary': 'The data consists of 3 four-type cylinders and 5 six-type cylinders, making 6 the mode. then, the discussion shifts to measures of spread.', 'duration': 31.348, 'max_score': 1524.723, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo1524723.jpg'}, {'end': 1614.443, 'src': 'embed', 'start': 1572.111, 'weight': 1, 'content': [{'end': 1577.257, 'text': 'It is the given measure of how spread apart the values in a data set are.', 'start': 1572.111, 'duration': 5.146}, {'end': 1581.799, 'text': 'The range can be calculated as shown in this formula.', 'start': 1577.797, 'duration': 4.002}, {'end': 1588.303, 'text': "So you're basically going to subtract the maximum value in your data set from the minimum value in your data set.", 'start': 1582.2, 'duration': 6.103}, {'end': 1594.327, 'text': "That's how you calculate the range of the data, all right? Next we have interquartile range.", 'start': 1588.703, 'duration': 5.624}, {'end': 1599.85, 'text': "So before we discuss interquartile range, let's understand what a quartile is.", 'start': 1595.067, 'duration': 4.783}, {'end': 1607.299, 'text': 'So quartiles basically tell us about the spread of a data set by breaking the data set into different quartiles.', 'start': 1600.855, 'duration': 6.444}, {'end': 1614.443, 'text': 'Just like how the median breaks the data into two parts, the quartile will break it into different quartiles.', 'start': 1608.059, 'duration': 6.384}], 'summary': 'The range measures spread by subtracting min from max. quartiles further break down data.', 'duration': 42.332, 'max_score': 1572.111, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo1572111.jpg'}, {'end': 1868.565, 'src': 'heatmap', 'start': 1722.076, 'weight': 3, 'content': [{'end': 1726.245, 'text': 'So guys, I hope all of you are clear with interquartile range and what are quartiles.', 'start': 1722.076, 'duration': 4.169}, {'end': 1728.891, 'text': "Now let's look at variance.", 'start': 1726.726, 'duration': 2.165}, {'end': 1736.132, 'text': 'Now, variance is basically a measure that shows how much a random variable differs from its expected value.', 'start': 1729.568, 'duration': 6.564}, {'end': 1739.034, 'text': "Okay, it's basically the variance in any variable.", 'start': 1736.492, 'duration': 2.542}, {'end': 1742.236, 'text': 'Now, variance can be calculated by using this formula.', 'start': 1739.494, 'duration': 2.742}, {'end': 1746.799, 'text': 'Right here, X basically represents any data point in your data set.', 'start': 1742.256, 'duration': 4.543}, {'end': 1753.083, 'text': 'N is the total number of data points in your data set, and X bar is basically the mean of data points.', 'start': 1747.359, 'duration': 5.724}, {'end': 1756.165, 'text': 'All right, this is how you calculate variance.', 'start': 1753.663, 'duration': 2.502}, {'end': 1760.209, 'text': 'Variance is basically computing the squares of deviations.', 'start': 1756.705, 'duration': 3.504}, {'end': 1762.21, 'text': "Okay, that's why it says s squared there.", 'start': 1760.449, 'duration': 1.761}, {'end': 1765.454, 'text': "Now let's look at what is deviation.", 'start': 1762.891, 'duration': 2.563}, {'end': 1769.117, 'text': 'Deviation is just the difference between each element from the mean.', 'start': 1765.874, 'duration': 3.243}, {'end': 1778.606, 'text': 'Okay, so it can be calculated by using this simple formula where xi basically represents a data point and mu is the mean of the population.', 'start': 1769.577, 'duration': 9.029}, {'end': 1781.649, 'text': 'Alright, this is exactly how you calculate deviation.', 'start': 1778.626, 'duration': 3.023}, {'end': 1791.481, 'text': "Now, population variance and sample variance are very specific to whether you're calculating the variance in your population data set or in your sample data set.", 'start': 1782.279, 'duration': 9.202}, {'end': 1794.702, 'text': "That's the only difference between population and sample variance.", 'start': 1791.841, 'duration': 2.861}, {'end': 1798.742, 'text': 'So the formula for population variance is pretty explanatory.', 'start': 1795.322, 'duration': 3.42}, {'end': 1800.983, 'text': 'So Xi is basically each data point.', 'start': 1799.103, 'duration': 1.88}, {'end': 1803.043, 'text': 'Mu is the mean of the population.', 'start': 1801.143, 'duration': 1.9}, {'end': 1805.764, 'text': 'N is the number of samples in your data set.', 'start': 1803.744, 'duration': 2.02}, {'end': 1809.125, 'text': "All right, now let's look at sample variance.", 'start': 1806.344, 'duration': 2.781}, {'end': 1813.227, 'text': 'Now sample variance is the average of squared differences from the mean.', 'start': 1809.626, 'duration': 3.601}, {'end': 1817.469, 'text': 'All right, here xi is any data point or any sample in your data set.', 'start': 1813.688, 'duration': 3.781}, {'end': 1820.171, 'text': 'X bar is the mean of your sample.', 'start': 1817.97, 'duration': 2.201}, {'end': 1822.572, 'text': "All right, it's not the mean of your population.", 'start': 1820.591, 'duration': 1.981}, {'end': 1824.173, 'text': "It's the mean of your sample.", 'start': 1823.052, 'duration': 1.121}, {'end': 1827.454, 'text': 'And if you notice, n here is a smaller n.', 'start': 1824.473, 'duration': 2.981}, {'end': 1829.635, 'text': "It's the number of data points in your sample.", 'start': 1827.454, 'duration': 2.181}, {'end': 1833.997, 'text': 'And this is basically the difference between sample and population variance.', 'start': 1830.556, 'duration': 3.441}, {'end': 1835.178, 'text': 'I hope that is clear.', 'start': 1834.378, 'duration': 0.8}, {'end': 1844.088, 'text': "Coming to standard deviation is the measure of dispersion of a set of data from its mean, all right? So it's basically the deviation from your mean.", 'start': 1835.996, 'duration': 8.092}, {'end': 1845.85, 'text': "That's what standard deviation is.", 'start': 1844.288, 'duration': 1.562}, {'end': 1852.099, 'text': "Now to better understand how the measures of spread are calculated, let's look at a small use case.", 'start': 1846.391, 'duration': 5.708}, {'end': 1855.021, 'text': "So let's say Daenerys has 20 dragons.", 'start': 1852.68, 'duration': 2.341}, {'end': 1860.542, 'text': 'They have the numbers nine, two, five, four, and so on, as shown on the screen.', 'start': 1855.421, 'duration': 5.121}, {'end': 1863.663, 'text': 'What you have to do is you have to work out the standard deviation.', 'start': 1860.822, 'duration': 2.841}, {'end': 1868.565, 'text': 'All right, in order to calculate the standard deviation, you need to know the mean right?', 'start': 1864.123, 'duration': 4.442}], 'summary': 'The transcript explains variance, deviation, population and sample variance, and standard deviation with calculation examples.', 'duration': 146.489, 'max_score': 1722.076, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo1722076.jpg'}, {'end': 1860.542, 'src': 'embed', 'start': 1835.996, 'weight': 4, 'content': [{'end': 1844.088, 'text': "Coming to standard deviation is the measure of dispersion of a set of data from its mean, all right? So it's basically the deviation from your mean.", 'start': 1835.996, 'duration': 8.092}, {'end': 1845.85, 'text': "That's what standard deviation is.", 'start': 1844.288, 'duration': 1.562}, {'end': 1852.099, 'text': "Now to better understand how the measures of spread are calculated, let's look at a small use case.", 'start': 1846.391, 'duration': 5.708}, {'end': 1855.021, 'text': "So let's say Daenerys has 20 dragons.", 'start': 1852.68, 'duration': 2.341}, {'end': 1860.542, 'text': 'They have the numbers nine, two, five, four, and so on, as shown on the screen.', 'start': 1855.421, 'duration': 5.121}], 'summary': 'Standard deviation measures data dispersion from its mean, illustrated with daenerys owning 20 dragons.', 'duration': 24.546, 'max_score': 1835.996, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo1835996.jpg'}], 'start': 1524.723, 'title': 'Measures of central tendency and spread', 'summary': "Covers measures of central tendency like mode and spread measures such as range and interquartile range, illustrated with a calculation example for quartiles of 100 students' marks. it also explains concepts like interquartile range, variance, deviation, population variance, and standard deviation, with a use case involving a sample set of numbers and an example of daenerys having 20 dragons resulting in a standard deviation of 2.983.", 'chapters': [{'end': 1690.059, 'start': 1524.723, 'title': 'Measures of spread and central tendency', 'summary': "Discusses the measures of central tendency, such as mode, and measures of spread, including range and interquartile range, using an example of calculating quartiles for a data set of 100 students' marks.", 'duration': 165.336, 'highlights': ['The mode is six since it is more recurrent than four, representing the measures of central tendency. The mode of six-type cylinders is more recurrent than the four-type cylinders.', 'The range is calculated by subtracting the minimum value from the maximum value in the data set. The range is calculated by subtracting the minimum value from the maximum value in the data set.', 'Quartiles break the data set into different parts, and the quartile values are calculated using the average of specific observations. Quartiles break the data set into different parts, and their values are calculated using the average of specific observations.']}, {'end': 1920.141, 'start': 1690.479, 'title': 'Interquartile range & variance', 'summary': 'Explains the concepts of interquartile range, variance, deviation, population variance, sample variance, standard deviation, and provides a use case for calculating standard deviation with a sample set of numbers, with an example of daenerys having 20 dragons, resulting in a standard deviation of 2.983.', 'duration': 229.662, 'highlights': ['The chapter explains the concepts of interquartile range, variance, deviation, population variance, sample variance, and standard deviation. It covers the key concepts discussed in the chapter.', 'Provides a use case for calculating standard deviation with a sample set of numbers, with an example of Daenerys having 20 dragons. It includes a practical example to illustrate the calculation of standard deviation.', "The standard deviation for the sample set of numbers (Daenerys' dragons) is calculated as 2.983. It quantifies the result of the standard deviation calculation."]}], 'duration': 395.418, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo1524723.jpg', 'highlights': ['The mode is six since it is more recurrent than four, representing the measures of central tendency.', 'The range is calculated by subtracting the minimum value from the maximum value in the data set.', 'Quartiles break the data set into different parts, and the quartile values are calculated using the average of specific observations.', 'The chapter explains the concepts of interquartile range, variance, deviation, population variance, sample variance, and standard deviation.', 'Provides a use case for calculating standard deviation with a sample set of numbers, with an example of Daenerys having 20 dragons.', "The standard deviation for the sample set of numbers (Daenerys' dragons) is calculated as 2.983."]}, {'end': 2934.584, 'segs': [{'end': 1961.041, 'src': 'embed', 'start': 1937.915, 'weight': 0, 'content': [{'end': 1945.08, 'text': "It's very important for you to know how information gain and entropy really work and why they're so essential in building machine learning models.", 'start': 1937.915, 'duration': 7.165}, {'end': 1948.978, 'text': "We'll focus on the statistic parts of information gain and entropy.", 'start': 1945.777, 'duration': 3.201}, {'end': 1955.64, 'text': "And after that, we'll discuss a use case and see how information gain and entropy is used in decision trees.", 'start': 1949.558, 'duration': 6.082}, {'end': 1961.041, 'text': "So for those of you who don't know what a decision tree is, it is basically a machine learning algorithm.", 'start': 1956.16, 'duration': 4.881}], 'summary': 'Understanding information gain and entropy in machine learning is essential for building models and decision trees.', 'duration': 23.126, 'max_score': 1937.915, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo1937915.jpg'}, {'end': 2076.1, 'src': 'embed', 'start': 2051.906, 'weight': 1, 'content': [{'end': 2060.252, 'text': 'So like I said, information gain and entropy are very important statistical measures that let us understand the significance of a predictive model.', 'start': 2051.906, 'duration': 8.346}, {'end': 2064.435, 'text': "To get a more clear understanding, let's look at a use case.", 'start': 2061.212, 'duration': 3.223}, {'end': 2069.036, 'text': "All right, now suppose we're given a problem statement.", 'start': 2066.333, 'duration': 2.703}, {'end': 2076.1, 'text': 'All right, the statement is that you have to predict whether a match can be played or not by studying the weather conditions.', 'start': 2069.416, 'duration': 6.684}], 'summary': 'Information gain and entropy help understand predictive models; example of predicting match based on weather.', 'duration': 24.194, 'max_score': 2051.906, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo2051906.jpg'}, {'end': 2213.197, 'src': 'embed', 'start': 2185.968, 'weight': 3, 'content': [{'end': 2189.408, 'text': 'Now when it comes to rain, we have three yeses and two noes.', 'start': 2185.968, 'duration': 3.44}, {'end': 2195.85, 'text': 'So if you notice here, the decision is being made by choosing the outlook variable as the root node.', 'start': 2190.629, 'duration': 5.221}, {'end': 2200.988, 'text': 'okay, so the root node is basically the topmost node in a decision tree.', 'start': 2196.445, 'duration': 4.543}, {'end': 2206.472, 'text': "now what we've done here is we've created a decision tree that starts with the outlook node.", 'start': 2200.988, 'duration': 5.484}, {'end': 2213.197, 'text': "all right, then you're splitting the decision tree further depending on other parameters like sunny, overcast and rain.", 'start': 2206.472, 'duration': 6.725}], 'summary': 'Decision tree based on outlook with 3 yeses and 2 noes', 'duration': 27.229, 'max_score': 2185.968, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo2185968.jpg'}, {'end': 2262.148, 'src': 'embed', 'start': 2228.592, 'weight': 2, 'content': [{'end': 2232.095, 'text': 'The root node is basically the topmost node in a decision tree.', 'start': 2228.592, 'duration': 3.503}, {'end': 2238.3, 'text': 'Now the outlook node has three branches coming out from it, which is sunny, overcast, and rain.', 'start': 2232.576, 'duration': 5.724}, {'end': 2241.082, 'text': 'So basically, outlook can have three values.', 'start': 2238.841, 'duration': 2.241}, {'end': 2244.305, 'text': 'Either it can be sunny, it can be overcast, or it can be rainy.', 'start': 2241.283, 'duration': 3.022}, {'end': 2252.9, 'text': 'Okay, now these three values are assigned to the immediate branch nodes and for each of these values the possibility of play is equal to.', 'start': 2244.853, 'duration': 8.047}, {'end': 2254.101, 'text': 'yes is calculated.', 'start': 2252.9, 'duration': 1.201}, {'end': 2262.148, 'text': 'So the sunny and the rain branches will give you an impure output, meaning that there is a mix of yes and no right?', 'start': 2254.781, 'duration': 7.367}], 'summary': 'Decision tree has outlook node with 3 branches: sunny, overcast, and rain. sunny and rain branches give impure output.', 'duration': 33.556, 'max_score': 2228.592, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo2228592.jpg'}, {'end': 2715.317, 'src': 'embed', 'start': 2675.177, 'weight': 4, 'content': [{'end': 2680.46, 'text': "Now guys, what is a confusion matrix? Now don't get confused, this is not any complex topic.", 'start': 2675.177, 'duration': 5.283}, {'end': 2686.763, 'text': 'Now a confusion matrix is a matrix that is often used to describe the performance of a model.', 'start': 2680.96, 'duration': 5.803}, {'end': 2692.326, 'text': 'And this is specifically used for classification models or a classifier.', 'start': 2687.723, 'duration': 4.603}, {'end': 2702.851, 'text': 'And what it does is it will calculate the accuracy or it will calculate the performance of your classifier by comparing your actual results and your predicted results.', 'start': 2692.946, 'duration': 9.905}, {'end': 2708.254, 'text': 'All right, so this is what it looks like, true positive plus true negative and all of that.', 'start': 2703.743, 'duration': 4.511}, {'end': 2710.352, 'text': 'Now this is a little confusing.', 'start': 2708.991, 'duration': 1.361}, {'end': 2715.317, 'text': "I'll get back to what exactly true positive, true negative and all of this stands for.", 'start': 2710.473, 'duration': 4.844}], 'summary': 'A confusion matrix evaluates classification model performance by comparing actual and predicted results.', 'duration': 40.14, 'max_score': 2675.177, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo2675177.jpg'}, {'end': 2813.301, 'src': 'embed', 'start': 2785.563, 'weight': 5, 'content': [{'end': 2788.966, 'text': "all right, that's pretty self-explanatory, but yeah.", 'start': 2785.563, 'duration': 3.403}, {'end': 2791.508, 'text': 'so it predicted that 110 times patient has a disease and 55 times that.', 'start': 2788.966, 'duration': 2.542}, {'end': 2792.849, 'text': "no, the patient doesn't have a disease.", 'start': 2791.508, 'duration': 1.341}, {'end': 2804.796, 'text': 'However, in reality, only 105 patients in the sample have the disease and 60 patients do not have the disease.', 'start': 2797.552, 'duration': 7.244}, {'end': 2811.36, 'text': 'So how do you calculate the accuracy of your model? You basically build the confusion matrix.', 'start': 2805.817, 'duration': 5.543}, {'end': 2813.301, 'text': 'This is how the matrix looks like.', 'start': 2811.7, 'duration': 1.601}], 'summary': 'Model predicted disease 110 times, actual disease present in 105 patients. accuracy calculated using confusion matrix.', 'duration': 27.738, 'max_score': 2785.563, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo2785563.jpg'}], 'start': 1921.704, 'title': 'Importance of statistical measures', 'summary': 'Discusses the importance of information gain and entropy in machine learning models, explaining their definitions, use in decision trees, and a use case scenario. it also explains the process of decision tree analysis using a dataset of 14 observations, and the concept of a confusion matrix used to evaluate the performance of a classifier in a classification model.', 'chapters': [{'end': 2135.478, 'start': 1921.704, 'title': 'Information gain and entropy', 'summary': 'Discusses the importance of information gain and entropy in machine learning models, explaining their definitions, use in decision trees, and a use case scenario, while emphasizing their significance as statistical measures for predictive models.', 'duration': 213.774, 'highlights': ['Information gain and entropy are essential in building machine learning models, especially in decision trees and random forest, and are crucial statistical measures for predictive models. The chapter emphasizes the importance of information gain and entropy in machine learning models, particularly in decision trees and random forest, serving as crucial statistical measures for predictive models.', 'Entropy is the measure of uncertainty in the data and can be calculated using a specific formula, while information gain indicates the amount of information a feature provides about the final outcome, with both being further clarified through a use case scenario. The concept of entropy as the measure of uncertainty in the data and the calculation formula, along with the explanation of information gain as the indicator of feature relevance, are provided and further elucidated through a use case scenario.', 'The use case involves predicting game feasibility based on weather conditions, where decision trees are employed to assess the significance of predictive models, demonstrating the practical application of information gain and entropy in a specific scenario. A use case scenario is presented, illustrating the application of information gain and entropy in predicting game feasibility based on weather conditions using decision trees, showcasing the practical relevance of these concepts in a specific context.']}, {'end': 2650.633, 'start': 2136.118, 'title': 'Decision tree analysis', 'summary': 'Explains the process of decision tree analysis using a dataset of 14 observations, where the outlook variable is chosen as the root node due to its 100% pure subset, and information gain is used to determine the most significant variable for splitting the data, resulting in the outlook variable being assigned as the root node with the highest information gain value of 0.247.', 'duration': 514.515, 'highlights': ['The outlook variable is chosen as the root node due to its 100% pure subset. The overcast value of the outlook variable results in a 100% pure subset, making it a significant variable for building the decision tree.', 'Information gain is used to determine the most significant variable for splitting the data. The information gain values for the attributes are calculated, and the outlook variable is chosen as the root node due to its highest information gain value of 0.247.', 'The decision tree analysis is performed using a dataset of 14 observations. The dataset consists of 14 observations, where the outlook variable is identified as the most significant variable for decision tree analysis.']}, {'end': 2934.584, 'start': 2651.153, 'title': 'Understanding confusion matrix', 'summary': 'Discusses the concept of a confusion matrix used to evaluate the performance of a classifier in a classification model, illustrated with an example of predicting disease in patients with a calculation of accuracy and interpretation of true positive, true negative, false positive, and false negative values.', 'duration': 283.431, 'highlights': ['A confusion matrix is used to describe the performance of a model and is specifically used for classification models or a classifier. It explains that a confusion matrix is utilized to evaluate the performance of a classification model.', 'Illustration of a use case where a classifier predicts disease in 165 patients, with 105 having the disease and 60 without it, and the classifier making predictions of yes and no for the patients. It provides an example of using a confusion matrix to calculate the accuracy of a classifier in predicting disease in patients.', 'Explanation of true positive, true negative, false positive, and false negative values and their interpretation in the context of the confusion matrix. It provides an interpretation of true positive, true negative, false positive, and false negative values in the context of a confusion matrix.']}], 'duration': 1012.88, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo1921704.jpg', 'highlights': ['Information gain and entropy are crucial in decision trees and random forest, serving as crucial statistical measures for predictive models.', 'The use case scenario illustrates the practical application of information gain and entropy in predicting game feasibility based on weather conditions using decision trees.', 'The outlook variable is chosen as the root node due to its 100% pure subset, making it a significant variable for building the decision tree.', 'The decision tree analysis is performed using a dataset of 14 observations, where the outlook variable is identified as the most significant variable for decision tree analysis.', 'A confusion matrix is utilized to evaluate the performance of a classification model.', 'It provides an example of using a confusion matrix to calculate the accuracy of a classifier in predicting disease in patients.', 'It provides an interpretation of true positive, true negative, false positive, and false negative values in the context of a confusion matrix.']}, {'end': 3775.467, 'segs': [{'end': 3021.646, 'src': 'embed', 'start': 2977, 'weight': 1, 'content': [{'end': 2983.363, 'text': "So guys, it's always best to perform practical implementations in order to understand the concepts in a better way.", 'start': 2977, 'duration': 6.363}, {'end': 2990.646, 'text': "Okay, so here we'll be executing a small demo that'll show you how to calculate the mean median mode, variance,", 'start': 2983.823, 'duration': 6.823}, {'end': 2994.968, 'text': 'standard deviation and how to study the variables by plotting a histogram.', 'start': 2990.646, 'duration': 4.322}, {'end': 2997.981, 'text': "Don't worry if you don't know what a histogram is.", 'start': 2996.08, 'duration': 1.901}, {'end': 2999.561, 'text': "It's basically a frequency plot.", 'start': 2998.061, 'duration': 1.5}, {'end': 3001.681, 'text': "There's no big science behind it.", 'start': 3000.121, 'duration': 1.56}, {'end': 3009.163, 'text': 'This is a very simple demo, but it also forms a foundation that every machine learning algorithm is built upon.', 'start': 3002.502, 'duration': 6.661}, {'end': 3015.765, 'text': 'You can say that most of the machine learning algorithms, actually all the machine learning algorithms and deep learning algorithms,', 'start': 3009.863, 'duration': 5.902}, {'end': 3018.005, 'text': 'have this basic concept behind them.', 'start': 3015.765, 'duration': 2.24}, {'end': 3021.646, 'text': 'You need to know how mean, median, mode, and all of that is calculated.', 'start': 3018.565, 'duration': 3.081}], 'summary': 'Practical demo on calculating mean, median, mode, variance, and standard deviation, forming foundation for machine learning.', 'duration': 44.646, 'max_score': 2977, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo2977000.jpg'}, {'end': 3126.435, 'src': 'embed', 'start': 3096.985, 'weight': 0, 'content': [{'end': 3102.248, 'text': 'Statistics is very easy when it comes to R because R is basically a statistical language.', 'start': 3096.985, 'duration': 5.263}, {'end': 3109.912, 'text': 'So all you have to do is just name the function and that function is already inbuilt in your R.', 'start': 3103.329, 'duration': 6.583}, {'end': 3112.691, 'text': 'So your median is around 6.4.', 'start': 3109.912, 'duration': 2.779}, {'end': 3115.652, 'text': "Similarly, we'll calculate the mode.", 'start': 3112.691, 'duration': 2.961}, {'end': 3118.493, 'text': "Let's run this function.", 'start': 3117.332, 'duration': 1.161}, {'end': 3121.533, 'text': 'I basically created a small function for calculating the mode.', 'start': 3118.553, 'duration': 2.98}, {'end': 3126.435, 'text': 'So guys, this is our mode, meaning that this is the most recurrent value.', 'start': 3122.254, 'duration': 4.181}], 'summary': 'R is an easy statistical language with inbuilt functions. median is 6.4, and mode is the most recurrent value.', 'duration': 29.45, 'max_score': 3096.985, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo3096985.jpg'}, {'end': 3238.071, 'src': 'embed', 'start': 3216.171, 'weight': 3, 'content': [{'end': 3225.232, 'text': 'Therefore, we can say that probability and statistics are interconnected branches of mathematics that deal with analyzing the relative frequency of events.', 'start': 3216.171, 'duration': 9.061}, {'end': 3232.394, 'text': "So they're very interconnected fields and probability makes use of statistics and statistics makes use of probability.", 'start': 3225.892, 'duration': 6.502}, {'end': 3234.194, 'text': "They're very interconnected fields.", 'start': 3232.894, 'duration': 1.3}, {'end': 3238.071, 'text': 'So that is the relationship between statistics and probability.', 'start': 3234.93, 'duration': 3.141}], 'summary': 'Probability and statistics are interconnected fields dealing with analyzing event frequency.', 'duration': 21.9, 'max_score': 3216.171, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo3216171.jpg'}, {'end': 3506.694, 'src': 'embed', 'start': 3474.443, 'weight': 2, 'content': [{'end': 3479.687, 'text': "I'll be talking about probability density function, normal distribution, and central limit theorem.", 'start': 3474.443, 'duration': 5.244}, {'end': 3491.982, 'text': 'Probability density function, also known as PDF, is concerned with the relative likelihood for a continuous random variable to take on a given value.', 'start': 3481.374, 'duration': 10.608}, {'end': 3492.263, 'text': 'all right?', 'start': 3491.982, 'duration': 0.281}, {'end': 3498.307, 'text': 'So the PDF gives the probability of a variable that lies between the range A and B.', 'start': 3492.543, 'duration': 5.764}, {'end': 3506.694, 'text': "So basically what you're trying to do is you're going to try and find the probability of a continuous random variable over a specified range.", 'start': 3498.307, 'duration': 8.387}], 'summary': 'Exploring probability density function, pdf, and its use in finding probabilities for continuous random variables.', 'duration': 32.251, 'max_score': 3474.443, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo3474443.jpg'}], 'start': 2935.085, 'title': 'Descriptive statistics in r', 'summary': 'Provides an introduction to descriptive statistics in r, covering mean, median, mode, variance, standard deviation, and histogram plotting. it also explains the relationship between statistics and probability, probability basics, terminologies, and probability distribution functions, including probability density function, normal distribution, and central limit theorem.', 'chapters': [{'end': 3021.646, 'start': 2935.085, 'title': 'Descriptive statistics demo in r', 'summary': 'Covers an introduction to descriptive statistics, a promise to run a demo in r to understand how mean median mode works, and the importance of practical implementation in understanding concepts in statistics.', 'duration': 86.561, 'highlights': ['The importance of practical implementation in understanding concepts in statistics, with a promise to run a demo in R to understand how mean median mode works.', 'The foundation of machine learning algorithms is built upon the concepts of mean, median, mode, and all of that is calculated, forming a basis for all machine learning and deep learning algorithms.', 'A small demo will be executed to show how to calculate the mean, median, mode, variance, standard deviation, and studying variables by plotting a histogram.']}, {'end': 3775.467, 'start': 3022.146, 'title': 'Descriptive statistics & probability basics', 'summary': 'Covers descriptive statistics in r, including mean, median, mode, variance, standard deviation, and histogram plotting, followed by an explanation of the relationship between statistics and probability, probability basics, and terminologies, such as random experiment, sample space, and event, along with an overview of probability distribution functions, including probability density function, normal distribution, and central limit theorem.', 'duration': 753.321, 'highlights': ['R provides functions for calculating mean, median, mode, variance, and standard deviation, making statistics tasks simpler for data scientists and analysts. R language simplifies statistical calculations by providing inbuilt functions, such as mean, median, mode, variance, and standard deviation, enhancing efficiency for data scientists and analysts.', 'Probability and statistics are interconnected fields, with probability being the measure of how likely an event will occur, presented as the ratio of desired outcome to total outcomes, and the probability summing up to one. Probability and statistics are interrelated, where probability measures the likelihood of an event and its occurrence, represented as a ratio, with the sum of all probabilities equating to one.', 'Probability basics are demonstrated using the example of rolling a dice, explaining the concept of probability, its range between zero and one, and the calculation of probabilities for specific outcomes. The concept of probability is illustrated using the example of rolling a dice, showcasing the range of probability values between zero and one, and the calculation of specific outcome probabilities, like rolling a three or a five.', 'Terminologies related to probability, such as random experiment, sample space, and event, are explained, providing clarity on their definitions and relevance in probability analysis. The definitions and significance of terminologies in probability, including random experiment, sample space, and event, are elucidated to enhance understanding and application in probability analysis.', 'The chapter delves into various probability distribution functions, including probability density function, normal distribution, and central limit theorem, offering detailed insights into their properties and applications. The discussion encompasses diverse probability distribution functions, such as probability density function, normal distribution, and central limit theorem, providing comprehensive details about their properties and practical usage.']}], 'duration': 840.382, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo2935085.jpg', 'highlights': ['R language simplifies statistical calculations by providing inbuilt functions, such as mean, median, mode, variance, and standard deviation, enhancing efficiency for data scientists and analysts.', 'The foundation of machine learning algorithms is built upon the concepts of mean, median, mode, and all of that is calculated, forming a basis for all machine learning and deep learning algorithms.', 'The chapter delves into various probability distribution functions, including probability density function, normal distribution, and central limit theorem, offering detailed insights into their properties and applications.', 'Probability and statistics are interrelated, where probability measures the likelihood of an event and its occurrence, represented as a ratio, with the sum of all probabilities equating to one.', 'A small demo will be executed to show how to calculate the mean, median, mode, variance, standard deviation, and studying variables by plotting a histogram.']}, {'end': 4212.675, 'segs': [{'end': 3809.041, 'src': 'embed', 'start': 3776.047, 'weight': 0, 'content': [{'end': 3780.991, 'text': "Now let's move on and look at our next topic, which is the different types of probability.", 'start': 3776.047, 'duration': 4.944}, {'end': 3786.874, 'text': 'Now, this is a important topic, because most of your problems can be solved by understanding.', 'start': 3781.511, 'duration': 5.363}, {'end': 3791.097, 'text': 'which type of probability should I use to solve this problem right?', 'start': 3786.874, 'duration': 4.223}, {'end': 3793.859, 'text': 'So we have three important types of probability.', 'start': 3791.717, 'duration': 2.142}, {'end': 3797.181, 'text': 'We have marginal, joint, and conditional probability.', 'start': 3794.159, 'duration': 3.022}, {'end': 3799.207, 'text': "So let's discuss each of these.", 'start': 3797.805, 'duration': 1.402}, {'end': 3809.041, 'text': 'Now the probability of an event occurring unconditioned on any other event is known as marginal probability or unconditional probability.', 'start': 3799.227, 'duration': 9.814}], 'summary': 'Different types of probability: marginal, joint, conditional. essential for problem-solving.', 'duration': 32.994, 'max_score': 3776.047, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo3776047.jpg'}, {'end': 4053.648, 'src': 'embed', 'start': 4010.61, 'weight': 1, 'content': [{'end': 4017.173, 'text': "So guys, basically you're comparing the salary package of a person depending on whether or not they've enrolled for Edureka training.", 'start': 4010.61, 'duration': 6.563}, {'end': 4018.874, 'text': 'This is our data set.', 'start': 4017.793, 'duration': 1.081}, {'end': 4021.495, 'text': "Now let's look at our problem statement.", 'start': 4019.454, 'duration': 2.041}, {'end': 4026.509, 'text': "Find the probability that a candidate has undergone Edureka's training.", 'start': 4022.219, 'duration': 4.29}, {'end': 4027.632, 'text': 'Quite simple.', 'start': 4026.99, 'duration': 0.642}, {'end': 4031.401, 'text': 'Which type of probability is this? This is marginal probability.', 'start': 4028.013, 'duration': 3.388}, {'end': 4039.835, 'text': "So the probability that a candidate has undergone Edureka's training is obviously 45 divided by 105.", 'start': 4032.789, 'duration': 7.046}, {'end': 4045.841, 'text': 'Since 45 is the number of candidates with Edureka training and 105 is the total number of candidates.', 'start': 4039.835, 'duration': 6.006}, {'end': 4049.824, 'text': 'So you get a value of approximately 0.42.', 'start': 4046.281, 'duration': 3.543}, {'end': 4053.648, 'text': "That's the probability of a candidate that has undergone Edureka's training.", 'start': 4049.824, 'duration': 3.824}], 'summary': "45 out of 105 candidates have undergone edureka's training, yielding a probability of 0.42.", 'duration': 43.038, 'max_score': 4010.61, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo4010610.jpg'}, {'end': 4149.965, 'src': 'embed', 'start': 4123.569, 'weight': 4, 'content': [{'end': 4131.496, 'text': "You're saying that you want to find the probability of a candidate who has a good package given that he's not undergone any training.", 'start': 4123.569, 'duration': 7.927}, {'end': 4133.957, 'text': "The condition is that he's not undergone any training.", 'start': 4131.515, 'duration': 2.442}, {'end': 4141.975, 'text': 'All right, so the number of people who have not undergone training are 60, and out of that, five of them have got a good package.', 'start': 4134.548, 'duration': 7.427}, {'end': 4149.965, 'text': "Right. so that's why this is five by 60 and not five by 105, because here they've clearly mentioned has a good package,", 'start': 4142.477, 'duration': 7.488}], 'summary': 'Probability of candidate with good package and no training: 5/60', 'duration': 26.396, 'max_score': 4123.569, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo4123569.jpg'}, {'end': 4212.675, 'src': 'embed', 'start': 4189.357, 'weight': 5, 'content': [{'end': 4199.806, 'text': "Those of you who aren't aware, naive bias is a supervised learning classification algorithm and it is mainly used in Gmail spam filtering.", 'start': 4189.357, 'duration': 10.449}, {'end': 4206.531, 'text': "A lot of you might have noticed that if you open up Gmail, you'll see that you have a folder called spam.", 'start': 4199.866, 'duration': 6.665}, {'end': 4212.675, 'text': 'All of that is carried out through machine learning and the algorithm used there is naive bias.', 'start': 4206.871, 'duration': 5.804}], 'summary': 'Naive bias is a supervised learning algorithm used for gmail spam filtering.', 'duration': 23.318, 'max_score': 4189.357, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo4189357.jpg'}], 'start': 3776.047, 'title': 'Probability types and training analysis', 'summary': "Discusses marginal, joint, and conditional probability with examples, and explores a use case of examining salary packages and training undergone by candidates. it also analyzes the probability of candidates receiving a good package based on their training status, yielding a probability of approximately 0.42 for edureka's training, 0.29 for both edureka's training and a good package, and a low probability of approximately 0.08 for a good package given no training. additionally, it introduces bayes' theorem and its application in naive bias algorithm for gmail spam filtering.", 'chapters': [{'end': 3961.754, 'start': 3776.047, 'title': 'Probability types and use case', 'summary': 'Discusses the three important types of probability - marginal, joint, and conditional probability, with specific examples and calculations, and then explores a use case of examining salary packages and training undergone by candidates.', 'duration': 185.707, 'highlights': ['The chapter discusses the three important types of probability - marginal, joint, and conditional probability, with specific examples and calculations. It explains the importance of understanding the different types of probability for problem-solving and provides clear definitions of marginal, joint, and conditional probability.', 'The probability of an event occurring unconditioned on any other event is known as marginal probability or unconditional probability, such as finding the probability that a card drawn is a heart (13/52). The concept of marginal probability is illustrated with a clear example of finding the probability of drawing a heart from a deck of 52 cards, resulting in a probability of 13/52.', 'Joint probability is a measure of two events happening at the same time, illustrated through the example of finding the probability of drawing a card that is four and red (1/26). The explanation of joint probability includes a specific example of finding the probability of two specific events happening simultaneously, resulting in a joint probability of 1/26.', 'Conditional probability is explained in the context of dependent and independent events, with corresponding expressions and definitions. The concept of conditional probability is described with clear distinctions between dependent and independent events, along with the respective expressions and definitions.', "The use case involves examining a data set on salary packages and training undergone by candidates. The chapter concludes with a practical use case of analyzing a data set related to candidates' salary packages and training, applying the concepts of probability discussed earlier."]}, {'end': 4212.675, 'start': 3962.455, 'title': 'Edureka training salary analysis', 'summary': "Discusses a survey of 105 candidates, with 60 without training and 45 enrolled in edureka's training, and analyzes the probability of candidates receiving a good package based on their training status, yielding a probability of approximately 0.42 for edureka's training, 0.29 for both edureka's training and a good package, and a low probability of approximately 0.08 for a good package given no training. additionally, it introduces bayes' theorem and its application in naive bias algorithm for gmail spam filtering.", 'duration': 250.22, 'highlights': ["The probability of a candidate has undergone Edureka's training is 45 divided by 105, yielding a value of approximately 0.42. Out of 105 candidates, 45 have undergone Edureka's training, resulting in a probability of approximately 0.42.", "The probability that a candidate has attended Edureka's training and also has a good package is 30 divided by 105, resulting in a probability of approximately 0.29. Out of 105 candidates, 30 have attended Edureka's training and have a good package, resulting in a probability of approximately 0.29.", 'The probability of a candidate has a good package given that he has not undergone training is 5 divided by 60, resulting in a probability of around 0.08. Out of 60 candidates without training, only 5 have a good package, resulting in a low probability of approximately 0.08.', "Introduces Bayes' Theorem and its application in naive bias algorithm for Gmail spam filtering. Bayes' Theorem is introduced as a concept used in naive bias algorithm for Gmail spam filtering, a supervised learning classification algorithm."]}], 'duration': 436.628, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo3776047.jpg', 'highlights': ['The chapter discusses the three important types of probability - marginal, joint, and conditional probability, with specific examples and calculations.', 'The use case involves examining a data set on salary packages and training undergone by candidates, applying the concepts of probability discussed earlier.', "The probability of a candidate has attended Edureka's training and also has a good package is 30 divided by 105, resulting in a probability of approximately 0.29.", "The probability of a candidate has undergone Edureka's training is 45 divided by 105, yielding a value of approximately 0.42.", 'The probability of a candidate has a good package given that he has not undergone training is 5 divided by 60, resulting in a probability of around 0.08.', "Introduces Bayes' Theorem and its application in naive bias algorithm for Gmail spam filtering."]}, {'end': 5802.895, 'segs': [{'end': 4240.772, 'src': 'embed', 'start': 4213.476, 'weight': 4, 'content': [{'end': 4217.739, 'text': "So now let's discuss what exactly the Bayes Theorem is and what it denotes.", 'start': 4213.476, 'duration': 4.263}, {'end': 4224.484, 'text': 'The Bayes Theorem is used to show the relation between one conditional probability and its inverse.', 'start': 4218.8, 'duration': 5.684}, {'end': 4234.229, 'text': "Basically, it's nothing but the probability of an event occurring based on prior knowledge of conditions that might be related to the same event.", 'start': 4225.985, 'duration': 8.244}, {'end': 4240.772, 'text': 'Mathematically, the Bayes theorem is represented like this, like shown in this equation.', 'start': 4235.369, 'duration': 5.403}], 'summary': 'Bayes theorem shows relation between conditional probabilities.', 'duration': 27.296, 'max_score': 4213.476, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo4213476.jpg'}, {'end': 4377.65, 'src': 'embed', 'start': 4350.626, 'weight': 3, 'content': [{'end': 4354.008, 'text': "So first of all, what we'll do is let's consider A.", 'start': 4350.626, 'duration': 3.382}, {'end': 4358.391, 'text': 'Let A be the event of picking a blue ball from bag A.', 'start': 4354.008, 'duration': 4.383}, {'end': 4361.813, 'text': 'And let X be the event of picking exactly two blue balls.', 'start': 4358.391, 'duration': 3.422}, {'end': 4365.855, 'text': 'Because these are the two events that we need to calculate the probability of.', 'start': 4362.513, 'duration': 3.342}, {'end': 4369.059, 'text': 'Now there are two probabilities that you need to consider here.', 'start': 4366.415, 'duration': 2.644}, {'end': 4377.65, 'text': 'One is the event of picking a blue ball from bag A and the other is the event of picking exactly two blue balls.', 'start': 4369.579, 'duration': 8.071}], 'summary': 'Calculate the probability of picking a blue ball from bag a and exactly two blue balls.', 'duration': 27.024, 'max_score': 4350.626, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo4350626.jpg'}, {'end': 5364.931, 'src': 'embed', 'start': 5334.529, 'weight': 2, 'content': [{'end': 5344.833, 'text': 'Now 75% is fairly high, so if John is not picked for three days in a row, the probability will drop down to approximately 42%.', 'start': 5334.529, 'duration': 10.304}, {'end': 5351.276, 'text': 'Okay, so three days in a row meaning that is the probability drops down to 42%.', 'start': 5344.833, 'duration': 6.443}, {'end': 5356.498, 'text': "Now let's consider a situation where John is not picked for 12 days in a row.", 'start': 5351.276, 'duration': 5.222}, {'end': 5359.678, 'text': 'the probability drops down to 3.2.', 'start': 5357.131, 'duration': 2.547}, {'end': 5364.931, 'text': "okay, that's the probability of john cheating becomes fairly high, right?", 'start': 5359.678, 'duration': 5.253}], 'summary': 'If john is not picked for 12 days in a row, the probability of him cheating becomes fairly high at 3.2%.', 'duration': 30.402, 'max_score': 5334.529, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo5334529.jpg'}, {'end': 5641.445, 'src': 'embed', 'start': 5614.948, 'weight': 1, 'content': [{'end': 5620.429, 'text': 'The p-value is very important measurement when it comes to ensuring the significance of a model.', 'start': 5614.948, 'duration': 5.481}, {'end': 5628.491, 'text': 'A model is said to be statistically significant only when the p-value is less than the predetermined statistical significance level,', 'start': 5620.829, 'duration': 7.662}, {'end': 5631.091, 'text': 'which is ideally 0.05..', 'start': 5628.491, 'duration': 2.6}, {'end': 5634.503, 'text': 'So your p-value has to be much lesser than 0.05.', 'start': 5631.091, 'duration': 3.412}, {'end': 5641.445, 'text': 'As you can see from our output, the p-value is very, very lesser when compared to 0.05.', 'start': 5634.503, 'duration': 6.942}], 'summary': 'P-value is crucial for model significance; should be much less than 0.05.', 'duration': 26.497, 'max_score': 5614.948, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo5614948.jpg'}, {'end': 5734.501, 'src': 'embed', 'start': 5708.208, 'weight': 0, 'content': [{'end': 5712.029, 'text': 'This is exactly what we discussed a couple of minutes ago.', 'start': 5708.208, 'duration': 3.821}, {'end': 5715.951, 'text': "Now just to end the demo, I'll show you a small visualization.", 'start': 5712.569, 'duration': 3.382}, {'end': 5725.255, 'text': "Here what we're doing is we're showing how the life expectancy for each continent varies with respect to the GDP per capita for that continent.", 'start': 5716.63, 'duration': 8.625}, {'end': 5729.137, 'text': 'Okay, so this is our plot.', 'start': 5726.816, 'duration': 2.321}, {'end': 5734.501, 'text': 'Okay, if you look at the illustration, you can almost see a linear variance right?', 'start': 5730.018, 'duration': 4.483}], 'summary': 'Demonstrated a visualization of life expectancy vs. gdp per capita for each continent.', 'duration': 26.293, 'max_score': 5708.208, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo5708208.jpg'}], 'start': 4213.476, 'title': 'Bayes theorem and statistical inference', 'summary': 'Explains bayes theorem through a practical example involving three bowls and the probability of drawing a blue ball. it also covers conditional probability, interval estimation, confidence intervals, and hypothesis testing, including practical applications and visualization using statistical datasets.', 'chapters': [{'end': 4350.086, 'start': 4213.476, 'title': 'Understanding bayes theorem', 'summary': 'Explains the bayes theorem, which is used to calculate the probability of an event based on prior knowledge, using a practical example involving three bowls and the probability of drawing a blue ball from a specific bowl.', 'duration': 136.61, 'highlights': ['The Bayes Theorem is used to calculate the probability of an event based on prior knowledge, such as the probability of drawing a blue ball from a specific bowl.', 'It involves understanding the likelihood ratio, posterior probability, and prior probability.', 'The practical example of drawing balls from three different bowls demonstrates the application of the Bayes Theorem in a real-world scenario.']}, {'end': 4929.319, 'start': 4350.626, 'title': 'Conditional probability and interval estimation in statistics', 'summary': 'Covers the conditional probability of picking a blue ball from bag a given picking exactly two blue balls, along with a detailed explanation of inferential statistics and interval estimation methods, including point estimation, confidence interval, and margin of error.', 'duration': 578.693, 'highlights': ['The chapter covers the conditional probability of picking a blue ball from bag A given picking exactly two blue balls. The explanation includes defining events A and X, calculating the probability of occurrence of event A given X using conditional probability, and finding the probabilities of A and X occurring together and probability of X.', 'The chapter provides a detailed explanation of inferential statistics and interval estimation methods. This includes a clear explanation of point estimation, methods for finding estimates such as method of moments, maximum likelihood, base estimator, and best unbiased estimators, as well as the importance of interval estimation, confidence interval, and margin of error.']}, {'end': 5269.125, 'start': 4929.319, 'title': 'Confidence intervals & hypothesis testing', 'summary': 'Explains confidence intervals, including the calculation of margin of error and estimation of confidence levels using critical values. additionally, it covers the concept and process of hypothesis testing in statistical inference.', 'duration': 339.806, 'highlights': ['Margin of error calculation formula The margin of error can be calculated using the formula: ZC multiplied by standard deviation divided by the square root of the sample size, where ZC represents the critical value or the confidence interval.', 'Estimation of confidence intervals The level of confidence is the probability that the interval estimate contains the population parameter, denoted by C. The area between -ZC and ZC represents the probability that the interval estimate contains the population parameter.', 'Steps involved in constructing a confidence interval The steps include identifying a sample statistic, selecting a confidence level, finding the margin of error, and specifying the confidence interval to estimate a population parameter.', 'Example of margin of error calculation In an example with a 95% confidence level and a sample size of 32, the margin of error for the mean prize of all textbooks in the bookstore is approximately 8.12, calculated using the formula.', 'Hypothesis testing process Hypothesis testing is used to determine if there is enough evidence in a data sample to infer a certain condition for an entire population, involving stating null and alternative hypotheses, formulating an analysis plan, analyzing sample data, and interpreting the results.']}, {'end': 5802.895, 'start': 5269.666, 'title': 'Probability and hypothesis testing', 'summary': 'Discusses the probability of john not cheating using hypothesis testing, demonstrates hypothesis testing using the gapminder dataset, and concludes with a visualization showing a linear variance between life expectancy and gdp per capita.', 'duration': 533.229, 'highlights': ['The probability of John not being picked for 12 days in a row drops down to approximately 3.2%, indicating a high probability of John cheating. The probability of John not being picked for 12 days in a row drops down to approximately 3.2%, suggesting a high likelihood of John cheating.', 'The p-value obtained from the t-test is very much lesser than 0.05, suggesting that the alternate hypothesis is true and the null hypothesis is disapproved. The p-value obtained from the t-test is very much lesser than 0.05, indicating that the alternate hypothesis is true and the null hypothesis is disapproved.', 'The demonstration concludes with a visualization showing a linear variance between life expectancy and GDP per capita, indicating a strong correlation between the two. The demonstration concludes with a visualization showing a linear variance between life expectancy and GDP per capita, indicating a strong correlation between the two.']}], 'duration': 1589.419, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XcLO4f1i4Yo/pics/XcLO4f1i4Yo4213476.jpg', 'highlights': ['The demonstration concludes with a visualization showing a linear variance between life expectancy and GDP per capita, indicating a strong correlation between the two.', 'The p-value obtained from the t-test is very much lesser than 0.05, indicating that the alternate hypothesis is true and the null hypothesis is disapproved.', 'The probability of John not being picked for 12 days in a row drops down to approximately 3.2%, suggesting a high likelihood of John cheating.', 'The chapter covers the conditional probability of picking a blue ball from bag A given picking exactly two blue balls.', 'The Bayes Theorem is used to calculate the probability of an event based on prior knowledge, such as the probability of drawing a blue ball from a specific bowl.']}], 'highlights': ['Statistics and probability are essential for machine learning, AI, and data science, forming the basic foundation for all related algorithms.', 'The chapter outlines the agenda for the session, covering key topics such as data types, descriptive statistics, probability distributions, and practical use cases.', 'The module will discuss the inferential statistics module, including point estimation, confidence interval, margin of error, and hypothesis testing.', 'The session will end by looking at a use case that discusses how hypothesis testing works.', 'After completing the probability module, the session will move on to the inferential statistics module.', 'The importance of data in various contexts, emphasizing its vital role in analysis and decision-making.', "Qualitative data encompasses characteristics and descriptors that can't be easily measured but can be observed subjectively, including nominal and ordinal data.", 'Statistics encompasses data collection, analysis, interpretation, and presentation, providing a comprehensive approach to understanding data.', 'Quantitative data comprises discrete (or categorical) data with finite values and continuous data with infinite possible values, such as the number of students in a class and the weight of a person, respectively.', 'Variables include discrete (or categorical) variables with different category values and continuous variables capable of storing an infinite range of values.', 'The chapter emphasizes the importance of understanding basic terminologies in statistics, such as population and sample, for analyzing real-world data.', 'The chapter illustrates how statistical techniques can be used to analyze sales data and identify areas for business improvement and growth.', 'The chapter emphasizes the use of statistical methods to conduct tests and confirm the effectiveness of a new drug, potentially benefiting cancer treatment.', 'The chapter highlights the application of statistics in evaluating the probability of a bet, offering a rational approach to decision-making in real-life scenarios.', 'The importance of sampling as a statistical method for inferring knowledge about a population is emphasized, particularly for practical data analysis.', 'Probability sampling techniques include random, systematic, and stratified sampling, ensuring equal chances of selection from the population.', 'Inferential statistics involves making predictions about a population based on sample data, using statistical models and sample data to infer data parameters and generalize findings.', 'Descriptive statistics involves measures of central tendency (mean, median, mode) and measures of variability (range, interquartile range, variance, standard deviation) to summarize and understand specific data sets.', 'The mode is six since it is more recurrent than four, representing the measures of central tendency.', 'The range is calculated by subtracting the minimum value from the maximum value in the data set.', 'Quartiles break the data set into different parts, and the quartile values are calculated using the average of specific observations.', 'The chapter explains the concepts of interquartile range, variance, deviation, population variance, sample variance, and standard deviation.', 'Provides a use case for calculating standard deviation with a sample set of numbers, with an example of Daenerys having 20 dragons.', "The standard deviation for the sample set of numbers (Daenerys' dragons) is calculated as 2.983.", 'Information gain and entropy are crucial in decision trees and random forest, serving as crucial statistical measures for predictive models.', 'The use case scenario illustrates the practical application of information gain and entropy in predicting game feasibility based on weather conditions using decision trees.', 'The outlook variable is chosen as the root node due to its 100% pure subset, making it a significant variable for building the decision tree.', 'The decision tree analysis is performed using a dataset of 14 observations, where the outlook variable is identified as the most significant variable for decision tree analysis.', 'A confusion matrix is utilized to evaluate the performance of a classification model.', 'It provides an example of using a confusion matrix to calculate the accuracy of a classifier in predicting disease in patients.', 'It provides an interpretation of true positive, true negative, false positive, and false negative values in the context of a confusion matrix.', 'R language simplifies statistical calculations by providing inbuilt functions, such as mean, median, mode, variance, and standard deviation, enhancing efficiency for data scientists and analysts.', 'The foundation of machine learning algorithms is built upon the concepts of mean, median, mode, and all of that is calculated, forming a basis for all machine learning and deep learning algorithms.', 'The chapter delves into various probability distribution functions, including probability density function, normal distribution, and central limit theorem, offering detailed insights into their properties and applications.', 'Probability and statistics are interrelated, where probability measures the likelihood of an event and its occurrence, represented as a ratio, with the sum of all probabilities equating to one.', 'A small demo will be executed to show how to calculate the mean, median, mode, variance, standard deviation, and studying variables by plotting a histogram.', 'The chapter discusses the three important types of probability - marginal, joint, and conditional probability, with specific examples and calculations.', 'The use case involves examining a data set on salary packages and training undergone by candidates, applying the concepts of probability discussed earlier.', "The probability of a candidate has attended Edureka's training and also has a good package is 30 divided by 105, resulting in a probability of approximately 0.29.", "The probability of a candidate has undergone Edureka's training is 45 divided by 105, yielding a value of approximately 0.42.", 'The probability of a candidate has a good package given that he has not undergone training is 5 divided by 60, resulting in a probability of around 0.08.', "Introduces Bayes' Theorem and its application in naive bias algorithm for Gmail spam filtering.", 'The demonstration concludes with a visualization showing a linear variance between life expectancy and GDP per capita, indicating a strong correlation between the two.', 'The p-value obtained from the t-test is very much lesser than 0.05, indicating that the alternate hypothesis is true and the null hypothesis is disapproved.', 'The probability of John not being picked for 12 days in a row drops down to approximately 3.2%, suggesting a high likelihood of John cheating.', 'The chapter covers the conditional probability of picking a blue ball from bag A given picking exactly two blue balls.', 'The Bayes Theorem is used to calculate the probability of an event based on prior knowledge, such as the probability of drawing a blue ball from a specific bowl.']}