Learn how to build recommender 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 technologies.

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.

• Building a recommendation engine
• Evaluating recommender systems
• Content-based filtering using item attributes
• Neighborhood-based collaborative filtering with user-based, item-based, and KNN CF
• Model-based methods including matrix factorization and SVD
• Applying deep learning, AI, and artificial neural networks to recommendations
• Session-based recommendations with recursive neural networks
• Scaling to massive data sets with Apache Spark machine learning, Amazon DSSTNE deep learning, and AWS SageMaker with factorization machines
• Real-world challenges and solutions with recommender systems
• Case studies from YouTube and Netflix
• Building hybrid, ensemble recommenders

This comprehensive course takes you all the way from the early days of collaborative filtering, to bleeding-edge applications of deep neural networks and modern machine learning techniques for recommending the best items to every individual user.

The coding exercises in this course use the Python programming language. We include an intro to Python if you’re new to it, but you’ll need some prior programming experience in order to use this course successfully. We also include a short introduction to deep learning if you are new to the field of artificial intelligence, but you’ll need to be able to understand new computer algorithms.

High-quality, hand-edited English closed captions are included to help you follow along.

I hope to see you in the course soon!