Building Recommender Systems with Machine Learning and AI

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

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

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