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This course explores the world of recommendation systems, which are used to provide personalized content and services to users. It covers the basics of these systems and delves into some of the most popular ones in detail. It also explains how various tools (e.g., machine learning, statistics, probability, and algebra) are used to analyze user data and provide personalized recommendations. This lesson is the first of a 3-part series on Deep Dive into Popular Recommendation Engines 101.