Building Recommender Systems with Machine Learning and AI

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

Created by: Sundog Education by Frank Kane
Join over 900K students learning ML, AI, AWS, and Data Eng.

Created by: Frank Kane
Ex-Amazon Sr. Engineer and Sr. Manager, CEO Sundog Education

Created by: Sundog Education Team
Sundog Education Team

What Will I 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)
• 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

• 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.

Target audience

• 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