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




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