Bayesian Machine Learning in Python: A/B Testing
4.6 (2,132 ratings)
13,927 students enrolled

# Bayesian Machine Learning in Python: A/B Testing

Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More
Bestseller
4.6 (2,132 ratings)
13,927 students enrolled
Last updated 10/2018
English
English [Auto-generated], Portuguese [Auto-generated], 1 more
Current price: \$11.99 Original price: \$119.99 Discount: 90% off
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This course includes
• 5.5 hours on-demand video
• Access on mobile and TV
• Certificate of Completion
Training 5 or more people?

What you'll learn
• Use adaptive algorithms to improve A/B testing performance

• ### Apply Bayesian methods to A/B testing

Requirements
• Probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF)
• Python coding with the Numpy stack
Description

This course is all about A/B testing.

A/B testing is used everywhere. Marketing, retail, newsfeeds, online advertising, and more.

A/B testing is all about comparing things.

If you’re a data scientist, and you want to tell the rest of the company, “logo A is better than logo B”, well you can’t just say that without proving it using numbers and statistics.

Traditional A/B testing has been around for a long time, and it’s full of approximations and confusing definitions.

In this course, while we will do traditional A/B testing in order to appreciate its complexity, what we will eventually get to is the Bayesian machine learning way of doing things.

You’ll learn about the epsilon-greedy algorithm, which you may have heard about in the context of reinforcement learning.

We’ll improve upon the epsilon-greedy algorithm with a similar algorithm called UCB1.

Finally, we’ll improve on both of those by using a fully Bayesian approach.

Why is the Bayesian method interesting to us in machine learning?

It’s an entirely different way of thinking about probability.

You’ll probably need to come back to this course several times before it fully sinks in.

It’s also powerful, and many machine learning experts often make statements about how they “subscribe to the Bayesian school of thought”.

In sum - it’s going to give us a lot of powerful new tools that we can use in machine learning.

The things you’ll learn in this course are not only applicable to A/B testing, but rather, we’re using A/B testing as a concrete example of how Bayesian techniques can be applied.

You’ll learn these fundamental tools of the Bayesian method - through the example of A/B testing - and then you’ll be able to carry those Bayesian techniques to more advanced machine learning models in the future.

See you in class!

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

• calculus

• probability (continuous and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule)

• Python coding: if/else, loops, lists, dicts, sets

• Numpy, Scipy, Matplotlib

TIPS (for getting through the course):

• Watch it at 2x.

• Take handwritten notes. This will drastically increase your ability to retain the information.

• Write down the equations. If you don't, I guarantee it will just look like gibberish.

• Ask lots of questions on the discussion board. The more the better!

• Realize that most exercises will take you days or weeks to complete.

• Write code yourself, don't just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

• Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

Who this course is for:
• Students and professionals with a technical background who want to learn Bayesian machine learning techniques to apply to their data science work
Course content
Expand all 53 lectures 05:31:45
+ Introduction and Outline
3 lectures 07:01
Preview 02:18
Where to get the code for this course
01:17
Preview 03:26
+ Bayes Rule and Probability Review
8 lectures 45:20
Bayes Rule Review
09:28
Simple Probability Problem
02:03
Preview 03:57
Preview 04:40
Maximum Likelihood - Mean of a Gaussian
04:52
Maximum Likelihood - Click-Through Rate
04:23
Confidence Intervals
10:11
05:46
14 lectures 01:01:15
A/B Testing Problem Setup
04:26
Simple A/B Testing Recipe
05:07
P-Values
03:53
Test Characteristics, Assumptions, and Modifications
06:45
t-test in Code
03:23
t-test Exercise
05:18
0.01 vs 0.011 - Why should we care?
01:46
A/B Test for Click-Through Rates (Chi-Square Test)
06:04
CTR A/B Test in Code
08:49
Chi-Square Exercise
02:33
A/B/C/D/… Testing - The Bonferroni Correction
02:20
Statistical Power
03:08
A/B Testing Pitfalls
04:01
03:42
+ Bayesian A/B Testing
11 lectures 54:20
Explore vs. Exploit
04:00
The Epsilon-Greedy Solution
02:58
UCB1
04:35
Conjugate Priors
07:04
Bayesian A/B Testing
04:10
Bayesian A/B Testing in Code
08:50
The Online Nature of Bayesian A/B Testing
02:31
Finding a Threshold Without P-Values
04:52
Thompson Sampling Convergence Demo
04:01
Confidence Interval Approximation vs. Beta Posterior
05:41
05:38
+ Practice Makes Perfect
5 lectures 18:32
Exercise: Compare different strategies
02:06
Intro to Exercises on Conjugate Priors
06:04
Exercise: Die Roll
02:38
Exercise: Gaussians
05:41
Exercise: Gaussian Implementation
02:03
+ Appendix
12 lectures 02:25:17
What is the Appendix?
02:48
Windows-Focused Environment Setup 2018
20:20
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
17:32
How to Code by Yourself (part 1)
15:54
How to Code by Yourself (part 2)
09:23
How to Succeed in this Course (Long Version)
10:24
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:04
Proof that using Jupyter Notebook is the same as not using it
12:29
Where to get Udemy coupons and FREE deep learning material
02:20
Python 2 vs Python 3
04:38
What order should I take your courses in? (part 1)
11:18
What order should I take your courses in? (part 2)
16:07