| Summary: | "Recommendation systems are a class of machine learning models with many applications. The idea behind recommendation systems is simple: filtering information to suggest items (anything from clothes to films) to users with the predicted probability that the users will enjoy such items. This course provides an introduction to recommendation systems. It starts by looking at the applications for these systems with a focus on the big companies whose fortune is built upon them. It then goes through a discussion of the different types of recommendation systems and how to implement them. You'll explore non-personalized systems, association rule learning, collaborative filtering, personalized systems, and the methods used to assess the quality (i.e., how good are the recommendations?) of a recommendation system. Learners should understand basic logic, supervised learning, and statistics."--Resource description page
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