Building Recommender Systems with Machine Learning and AI

Building Recommender Systems with Machine Learning and AI

Updated with Tensorflow Recommenders (TFRS) and Generative Adversarial Networks for recommendations (GANs)

Learn how to build machine learning recommendation 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 systems.

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

• Using the latest frameworks from Tensorflow (TFRS) and Amazon Personalize.

• 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

• "Bleeding edge alerts" covering the latest research in the field of recommender systems

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!

How to create machine learning recommendation systems with deep learning, collaborative filtering, and Python.

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What you will 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 recommendation engines with neural networks and Restricted Boltzmann Machines (RBM's)

Rating: 4.52846

Level: Intermediate Level

Duration: 11.5 hours

Instructor: Sundog Education by Frank Kane