This course is all about A/B testing. A/B testing is used everywhere. 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.

A/B testing is used everywhere. 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.

Free Coupon Discount - 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 Programmer Inc. Students also bought Data Science: Deep Learning in Python Deep Learning Prerequisites: Logistic Regression in Python The Complete Neural Networks Bootcamp: Theory, Applications TensorFlow 2.0 Practical Advanced Deep Learning: Advanced NLP and RNNs Preview this Udemy Course GET COUPON CODE Description This course is all about A/B testing. A/B testing is used everywhere. 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.

Bayesian Machine Learning in Python: A/B Testing 4.5 (3,363 ratings) Course Ratings are calculated from individual students' ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. This course is all about A/B testing. A/B testing is used everywhere. 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.