Building Recommendation Systems with Python (TTAML0002)

Recommendation systems are at the heart of almost every internet business today, from Facebook to Netflix to Amazon. They are providing good recommendations, whether its friends, movies, or groceries, that go a long way in defining user experience and enticing customers to use your platform.

This course shows you how to do just that. You'll learn the different kinds of recommenders used in the industry and how to build them from scratch using Python. No need to wade through tons of machine learning theory, you'll get started with building and learning about recommenders quickly. In this course, you'll build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content-based and collaborative filtering techniques.

Join us to learn how to build industry-standard recommender systems, leveraging Python syntax skills. This is an applied AI course, so machine learning theory is only used to highlight how to build recommenders in this course.