What you'll learn

Understand and apply user-based and item-based collaborative filtering to recommend items to users

Create recommendations using deep learning at massive scale

Build recommender systems with neural networks and Restricted Boltzmann Machines (RBM's)

Make session-based recommendations with recurrent neural networks and Gated Recurrent Units (GRU)

Build a framework for testing and evaluating recommendation algorithms with Python

Apply the right measurements of a recommender system's success

Build recommender systems with matrix factorization methods such as SVD and SVD++

Apply real-world learnings from Netflix and YouTube to your own recommendation projects

Combine many recommendation algorithms together in hybrid and ensemble approaches

Use Apache Spark to compute recommendations at large scale on a cluster

Use K-Nearest-Neighbors to recommend items to users

Solve the "cold start" problem with content-based recommendations

Understand solutions to common issues with large-scale recommender systems

Requirements

Some experience with a programming or scripting language (preferably Python)

Some computer science background, and an ability to understand new algorithms.

Description

New! Updated for Tensorflow 2, Personalize, and more.

Learn how to build recommender systems from one of 's pioneers in the field. Frank Kane spent over nine years at , where he managed and led the development of many of 's personalized product recommendation technologies.

You've seen automated recommendations everywhere - on Netflix's home page, on YouTube, and on 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 ee

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, 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!

Who this course is for:

Software developers interested in applying machine learning and deep learning to product or content recommendations

Eeers working at, or interested in working at large e-commerce or web companies

Computer Scientists interested in the latest recommender system theory and research



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