This is a high-level list of topics covered in this course. Please see the detailed Agenda below • Get to grips with the different kinds of recommender systems • Master data-wrangling techniques using the pandas library • Building an IMDB Top 250 Clone • Build a content-based engine to recommend movies based on movie metadata • Employ data-mining techniques used in building recommenders • Build industry-standard collaborative filters using powerful algorithms • Building Hybrid Recommenders that incorporate content based and collaborative fltering
Recommendation systems are at the heart of almost every internet business today; from Facebook to Netﬂix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see 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 as quickly as possible. In this course, you will 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 Working in a hands-on learning environment, led by our Recommendation Systems expert instructor, students will learn about and explore: • Build industry-standard recommender systems • Only familiarity with Python is required • No need to wade through complicated machine learning theory to use this course.