Building Recommender Systems with Machine Learning and AI

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


  • 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.

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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