Bayesian Machine Learning in Python: A/B Testing
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# Bayesian Machine Learning in Python: A/B Testing

Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More
Best Seller
4.6 (394 ratings)
5,214 students enrolled
Last updated 8/2017
English
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Price: \$120
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Includes:
• 3.5 hours on-demand video
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

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What Will I Learn?
• Use adaptive algorithms to improve A/B testing performance
• Understand the difference between Bayesian and frequentist statistics
• Apply Bayesian methods to A/B testing
View Curriculum
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.

First, we’ll see if we can improve on traditional A/B testing with adaptive methods. These all help you solve the explore-exploit dilemma.

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.

It’s a paradigm shift.

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!

All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples

In the directory: ab_testing

Make sure you always "git pull" so you have the latest version!

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.

USEFUL COURSE ORDERING:

• (The Numpy Stack in Python)
• Linear Regression in Python
• Logistic Regression in Python
• (Supervised Machine Learning in Python)
• (Bayesian Machine Learning in Python: A/B Testing)
• Deep Learning in Python
• Practical Deep Learning in Theano and TensorFlow
• (Supervised Machine Learning in Python 2: Ensemble Methods)
• Convolutional Neural Networks in Python
• (Easy NLP)
• (Cluster Analysis and Unsupervised Machine Learning)
• Unsupervised Deep Learning
• (Hidden Markov Models)
• Recurrent Neural Networks in Python
• Artificial Intelligence: Reinforcement Learning in Python
• Natural Language Processing with Deep Learning in Python
• Advanced AI: Deep Reinforcement Learning in Python

Who is the target audience?
• Students and professionals with a technical background who want to learn Bayesian machine learning techniques to apply to their data science work
Compare to Other Python Courses
Curriculum For This Course
40 Lectures
03:30:08
+
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 45:26
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
10:17

What is the Bayesian Paradigm?
05:46
+
Traditional A/B Testing
12 Lectures 53:25
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

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
08:50

A/B/C/D/… Testing - The Bonferroni Correction
02:20

Statistical Power
03:08

A/B Testing Pitfalls
04:01

Traditional A/B Testing Summary
03:42
+
Bayesian A/B Testing
10 Lectures 48:42
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
+
Practice Makes Perfect
3 Lectures 10:25
Exercise: Compare different strategies
02:06

Exercise: Die Roll
02:38

Exercise: Multivariate Gaussian Likelihood
05:41
+
Appendix
4 Lectures 45:09
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

Preview 02:20
About the Instructor
 4.6 Average rating 14,439 Reviews 76,607 Students 19 Courses
Data scientist and big data engineer

I am a data scientist, big data engineer, and full stack software engineer.

I have a masters degree in computer engineering with a specialization in machine learning and pattern recognition.

I have worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.