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

Angie Ma, Gary Willis, and Alessandra Stagliano are data scientists with ASI Data Science, a London based AI/machine learning solutions firm. Angie co-founded ASI and is also the founder of Data Science Lab London, one of the biggest communities of data scientists and data engineers in Europe, with over 2,500 members. Angie holds a PhD in physics from London's University College, Gary Willis holds a PhD in statistical physics from London's Imperial College, and Alessandra Stagliano holds a PhD in computer science from the University of Genoa. Collectively, the group has worked on over 150 commercial AI/machine learning projects.