Bayesian Machine Learning in Python: A/B Testing
Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More
Created by Lazy Programmers Inc | 10.5 hours on-demand video course
This Bayesian Machine Learning in Python 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 Bayesian Machine Learning in Python: A/B Testing 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.
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
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