Are you curious about how Netflix, Amazon and YouTube make their recommendations? Do you want to learn how to build a Recommendation Engine from scratch? This course is the perfect place to start! You will learn the basic concepts of a Recommendation Engine and be able to build a simple but functional one using simple math and basic Python programming skills. The course will walk you through the Collaborative Filtering algorithm used by Netflix to recommend movies to users based on the tastes of other similar users. So join now and make your first big steps towards the future!

This course is perfect for anyone interested in learning how to create a Music Recommender System using Pyspark. You'll learn how to use the ALS Algorithm to suggest new tracks to different users based on the songs they've been listening to. As a prerequisite, it's suggested that you have a basic understanding of the ALS Algorithm. This course is best suited for learners based in the North America region, but we're working on providing the same experience in other regions. So don't miss out on this great opportunity to learn how to create a Music Recommender System using Pyspark!

This 1-hour project-based course on Building Similarity Based Recommendation System is perfect for learners based in North America. Learn how to create user item interactions matrix from the original dataset and recommend items to a new user. Understand how similarity based collaborative filtering recommendation systems work and how to compute similarity between users. Get the skills to collect data for building such systems and recommend items based on products interacted by other similar users. Don't miss out on this great opportunity to learn and grow your skills!

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