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

HOMEPAGE

https://www.lynda.com/Python-tutorials/Building-Recommender-Systems-Machine-Learning-AI/5028662-2.html