Learning objectives 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