Bayesian Machine Learning in Python: A/B Testing (updated 8/2020)

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Bayesian Machine Learning in Python: A/B Testing
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 44100 Hz
Language: English | VTT | Size: 2.08 GB | Duration: 11 section | 73 lectures | (8h 52m)

What you'll 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

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.

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