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Bayesian Machine Learning in Python: A/B Testing
Introduction and Outline
What's this course all about? (3:55)
Special Offer! Get the VIP version of this course (1:14)
Where to get the code for this course (5:01)
How to succeed in this course (5:51)
The High-Level Picture
Real-World Examples of A/B Testing (6:46)
What is Bayesian Machine Learning? (11:33)
Bayes Rule and Probability Review
Review Section Introduction (1:22)
Probability and Bayes' Rule Review (5:27)
Calculating Probabilities - Practice (10:25)
The Gambler (5:42)
The Monty Hall Problem (7:01)
Maximum Likelihood Estimation - Bernoulli (11:42)
Click-Through Rates (CTR) (2:08)
Maximum Likelihood Estimation - Gaussian (pt 1) (10:07)
Maximum Likelihood Estimation - Gaussian (pt 2) (8:40)
CDFs and Percentiles (9:38)
Probability Review in Code (10:24)
Probability Review Section Summary (5:12)
Beginners: Fix Your Understanding of Statistics vs Machine Learning (6:47)
Suggestion Box (3:03)
Traditional A/B Testing
Confidence Intervals (pt 1) - Intuition (5:09)
Confidence Intervals (pt 2) - Beginner Level (4:45)
Confidence Intervals (pt 3) - Intermediate Level (10:25)
Confidence Intervals (pt 4) - Intermediate Level (11:42)
Confidence Intervals (pt 5) - Intermediate Level (10:08)
Confidence Intervals Code (6:32)
Hypothesis Testing - Examples (7:15)
Statistical Significance (5:26)
Hypothesis Testing - The API Approach (9:17)
Hypothesis Testing - Accept Or Reject? (2:23)
Hypothesis Testing - Further Examples (4:59)
Z-Test Theory (pt 1) (8:47)
Z-Test Theory (pt 2) (8:30)
Z-Test Code (pt 1) (13:02)
Z-Test Code (pt 2) (5:54)
A/B Test Exercise (3:54)
Classical A/B Testing Section Summary (9:57)
Bayesian A/B Testing
Section Introduction: The Explore-Exploit Dilemma (10:17)
Applications of the Explore-Exploit Dilemma (8:00)
Epsilon-Greedy Theory (7:04)
Calculating a Sample Mean (pt 1) (5:56)
Epsilon-Greedy Beginner's Exercise Prompt (5:05)
Designing Your Bandit Program (4:09)
Epsilon-Greedy in Code (7:12)
Comparing Different Epsilons (6:02)
Optimistic Initial Values Theory (5:40)
Optimistic Initial Values Beginner's Exercise Prompt (2:26)
Optimistic Initial Values Code (4:18)
UCB1 Theory (14:32)
UCB1 Beginner's Exercise Prompt (2:14)
UCB1 Code (3:28)
Bayesian Bandits / Thompson Sampling Theory (pt 1) (12:43)
Bayesian Bandits / Thompson Sampling Theory (pt 2) (17:35)
Thompson Sampling Beginner's Exercise Prompt (2:50)
Thompson Sampling Code (5:03)
Thompson Sampling With Gaussian Reward Theory (11:24)
Thompson Sampling With Gaussian Reward Code (6:18)
Why don't we just use a library? (5:40)
Nonstationary Bandits (7:11)
Bandit Summary, Real Data, and Online Learning (6:10)
Practice Makes Perfect
Exercise: Compare different strategies (2:06)
Exercise: Die Roll (2:38)
Exercise: Multivariate Gaussian Likelihood (5:41)
Appendix
What order should I take your courses in? (pt 1) (11:18)
What order should I take your courses in? (pt 2) (16:07)
How to Code by Yourself (part 1) (15:54)
How to Code by Yourself (part 2) (9:23)
How to Succeed in this Course (Long Version) (10:24)
BONUS: Where to get discount coupons and FREE deep learning material (5:31)
Exercise: Multivariate Gaussian Likelihood
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