Building Recommender Systems with Machine Learning and AI

About this course

Updated with Neural Collaborative Filtering (NCF), Tensorflow Recommenders (TFRS) and Generative Adversarial Networks for recommendations (GANs)Learn how to build machine learning recommendation systems from one of Amazon's pioneers in the field. Frank Kane spent over nine years at Amazon, where he managed and led the development of many of Amazon's personalized product recommendation systems. You've seen automated recommendations everywhere - on Netflix's home page, on YouTube, and on Amazon as these machine learning algorithms learn about your unique interests, and show the best products or content for you as an individual. These technologies have become central to the largest, most prestigious tech employers out there, and by understanding how they work, you'll become very valuable to them.

We'll cover tried and true recommendation algorithms based on neighborhood-based collaborative filtering, and work our way up to more modern techniques including matrix factorization and even deep learning with artificial neural networks. Along the way, you'll learn from Frank's extensive industry experience to understand the real-world challenges you'll encounter when applying these algorithms at large scale and with real-world data.

However, this course is very hands-on; you'll develop your own framework for evaluating and combining many different recommendation algorithms together, and you'll even build your own neural networks using Tensorflow to generate recommendations from real-world movie ratings from real people. We'll cover:

• Building a recommendation engine
• Evaluating recommender systems
• Content-based filtering using item attributes
• Neighborhood-based collaborative filtering with user-based, item-based, and KNN CFModel-based methods including

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