Frank Kane spent over nine years at Amazon, where he managed and led the development of many of Amazon’s personalized product recommendation systems . We’ll cover tried and true recommendation algorithms based on neighborhood-based collaborative filtering, and work up to more modern techniques including matrix factorization and deep learning with artificial neural networks . Along the way, you’ll learn from Frank’s extensive industry experience to understand the challenges you’ll encounter when applying these algorithms at large scale and with real-world data . Use Apache Spark to compute recommendations at large-scale on a cluster . Use K-Nearest-Neighbors to recommend items to users . Solve the “cold start” problem with content-based recommendations .Authentication failed. Unique API key is not valid for this user.
Who this course is for:
Software developers interested in applying machine learning and deep learning to product or content recommendations
Engineers working at, or interested in working at large e-commerce or web companies
Computer Scientists interested in the latest recommender system theory and research
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Building Recommender Systems with Machine Learning and AI free download