This course, Building Recommender Systems with Machine Learning and AI, taught by Amazon's pioneer in the field, Frank Kane, will teach you how to create machine learning recommendation systems with deep learning, collaborative filtering, and Python. You'll learn to understand and apply user-based and item-based collaborative filtering, create recommendations using deep learning, build recommendation engines with neural networks, make session-based recommendations with recurrent neural networks, and more. You'll also learn to apply real-world learnings from Netflix and YouTube to your own recommendation projects, combine many recommendation algorithms together in hybrid and ensemble approaches, and use Apache Spark to compute recommendations at large scale on a cluster. This course is perfect for those looking to become valuable to the largest, most prestigious tech employers.

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!