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Building Recommender Systems with Machine Learning and AI

Find out how to create suggestion programs with deep studying, collaborative filtering, and machine studying.

What you’ll study

  • Perceive and apply user-based and item-based collaborative filtering to suggest objects to customers
  • Create suggestions utilizing deep studying at large scale
  • Construct recommender programs with neural networks and Restricted Boltzmann Machines (RBM’s)
  • Make session-based suggestions with recurrent neural networks and Gated Recurrent Models (GRU)
  • Construct a framework for testing and evaluating suggestion algorithms with Python
  • Apply the proper measurements of a recommender system’s success
  • Construct recommender programs with matrix factorization strategies similar to SVD and SVD++
  • Apply real-world learnings from Netflix and YouTube to your individual suggestion tasks
  • Mix many suggestion algorithms collectively in hybrid and ensemble approaches
  • Use Apache Spark to compute suggestions at massive scale on a cluster
  • Use Ok-Nearest-Neighbors to suggest objects to customers
  • Resolve the “chilly begin” drawback with content-based suggestions
  • Perceive options to frequent points with large-scale recommender programs

Requirements

  • A Home windows, Mac, or Linux PC with not less than 3GB of free disk house.
  • Some expertise with a programming or scripting language (ideally Python)
  • Some laptop science background, and a capability to know new algorithms.

Who this course is for:

  • Software program builders occupied with making use of machine studying and deep studying to product or content material suggestions
  • Engineers working at, or occupied with working at massive e-commerce or internet corporations
  • Laptop Scientists within the newest recommender system concept and analysis


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