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.
Understand how recommendation systems work and how they are applied Learn the difference between personalized and non-personalized recommendation systems Discover the distinctions between content-based and user-based recommendation systems Learn how to use - and enjoy free access to - the SherlockML data science platform Develop the skills required for the machine learning job market, where demand outstrips supply
Download link : (If you need these, buy and download immediately before they are delete)