Automated recommendations are everywhere: Netflix, Amazon, YouTube, and more. Recommender systems learn about your unique interests and show the products or content they think you'll like best. Discover how to build your own recommender systems from one of the pioneers in the field. Frank Kane spent over nine years at Amazon, where he led the development of many of the company's personalized product recommendation technologies. In this course, he covers recommendation algorithms based on neighborhood-based collaborative filtering and more modern techniques, including matrix factorization and even deep learning with artificial neural networks. Along the way, you can learn from Frank's extensive industry experience and understand the real-world challenges of applying these algorithms at a large scale with real-world data. You can also go hands-on, developing your own framework to test algorithms and building your own neural networks using technologies like Amazon DSSTNE, AWS SageMaker, and TensorFlow.

Topics include:

Top-N recommender architectures
Types of recommenders
Python basics for working with recommenders
Evaluating recommender systems
Measuring your recommender
Reviewing a recommender engine framework
Content-based filtering
Neighborhood-based collaborative filtering
Matrix factorization methods
Deep learning basics
Applying deep learning to recommendations
Scaling with Apache Spark, Amazon DSSTNE, and AWS SageMaker
Real-world challenges and solutions with recommender systems
Case studies from YouTube and Netflix
Building hybrid, ensemble recommenders

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