Bayesian Machine Learning in Python: A/B Testing
A/B Bayesian Machine Learning in Python Comparing things is at the heart of testing. The Bayesian approach is a completely different way of approaching probability. It is also very effective, and many machine learning experts frequently mention that they "subscribe to the Bayesian school of thought" in their statements.
First, you'll test whether using adaptive methods can make traditional A/B testing better in this course. You can overcome the explore-exploit conundrum using any of these. You will discover the epsilon-greedy algorithm, which you might be familiar with from reinforcement learning. UCB1, a related algorithm, will also enhance the epsilon-greedy algorithm. And finally, if you take a fully Bayesian approach, you'll outperform both of those.
While you will perform conventional A/B testing in this course in order to understand its complexity, you will eventually get to the Bayesian machine learning approach to problem solving. This is definitey one of the best online Bayesian courses.
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
Requirements
- Probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF)
- Python coding with the Numpy stack
Course ratings: 4.6/5
Enroll here: https://www.udemy.com/course/bayesian-machine-learning-in-python-ab-testing/
