3. Foundations of Statistics and Probability for Machine Learning (Janani Ravi, 2021)

1. Course Overview: 1. Course Overview 00:00:00 2. Understanding Descriptive Statistics and Probability Distributions: 01. Version Check 00:01:55 02. Prerequisites and Course Outline 00:02:11 03. Descriptive Statistics to Understand Data 00:04:42 04. Measures of Frequency and Central Tendency 00:08:27 05. Measures of Dispersion 00:15:18 06. Demo - Measures of Central Tendency 00:19:14 07. Demo - Measures of Dispersion 00:25:01 08. Probability and the Gaussian Normal Distribution 00:29:07 09. Demo - Probability 00:33:31 10. Demo - Normal Distribution 00:36:35 11. Skewness and Kurtosis 00:41:09 12. Demo - Skewness and Kurtosis 00:45:40 3. Interpreting Data Using Statistical Test: 1. Steps in Hypothesis Testing 00:53:45 2. Hypothesis Testing - Lady Tasting Tea 01:00:14 3. Type I and Type II Errors 01:03:51 4. Introducing t-tests 01:08:27 5. Types of t-tests 01:12:53 6. Demo - Two Sample t-test Part I 01:18:28 7. Demo - Two Sample t-test Part II 01:25:26 8. Demo - Paired Samples t-test 01:28:16 4. Performing Regression Analysis: 1. Connecting the Dots with Linear Regression 01:36:19 2. Setting up the Regression Problem 01:42:35 3. Interpreting the Results of Regression 01:46:45 4. Demo - Exploring the Dataset 01:50:55 5. Demo - Regression Analysis Using a Single Predictor 01:53:43 6. Demo - Preprocessing Data for Multiple Regression 02:00:55 7. Demo - Regression Analysis Using Multiple Predictors 02:07:19 8. Summary and Further Study 02:11:57