A/B testing is used everywhere, from marketing, retail, news feeds, online advertising, and much more. If you’re a data scientist, and you want to tell the rest of the company, “logo A is better than logo B,” you’re going to need numbers and stats to prove it. That’s where A/B testing comes in. In this course, you’ll do traditional A/B testing in order to appreciate its complexity as you elevate towards the Bayesian machine learning way of doing things.
- Access 40 lectures & 3.5 hours of content 24/7
- Improve on traditional A/B testing w/ adaptive methods
- Learn about epsilon-greedy algorithm & improve upon it w/ a similar algorithm called UCB1
- Understand how to use a fully Bayesian approach to A/B testing
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