Help people discover new products and content with deep learning, neural networks, and machine learning recommendations. Includes 9.5 hours of on-demand video and a certificate of completion.

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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.

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.

Recommender systems are complex; don’t enroll in this course expecting a learn-to-code type of format. There’s no recipe to follow on how to make a recommender system; you need to understand the different algorithms and how to choose when to apply each one for a given situation. We assume you already know how to code.

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 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!

Course Instructor

Frank Kane Frank Kane Author

Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.

Buy This Course

$39.99

Lifetime access to all videos and materials for this course with a one-time payment.

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Getting Started

Introduction to Python [Optional]

Evaluating Recommender Systems

A Recommender Engine Framework

Content-Based Filtering

Neighborhood-Based Collaborative Filtering

Matrix Factorization Methods

Introduction to Deep Learning [Optional]

Deep Learning for Recommender Systems

Scaling it Up

Real-World Challenges of Recommender Systems

Case Studies

Hybrid Approaches

Wrapping Up