Building Recommender Systems with Machine Learning and AI

Building Recommender Systems with Machine Learning and AI
Video: .mp4 (1280×720, 30 fps(r)) | Audio: aac, 44100 Hz, 2ch | Size: 4.44 GB

Help people discover new products and content with deep learning, neural networks, and machine learning recommendations.

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
Understand solutions to common issues with large-scale recommender systems

Requirements

A Windows, Mac, or Linux PC with at least 3GB of free disk space.
Some experience with a programming or scripting language (preferably Python)
Some computer science background, and an ability to understand new algorithms.

Description

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.

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

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.

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
Engineers 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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