Recommendation Systems – Google

Interested in Machine Learning ? This free online course on Recommendation Systems from Google experts is the best place to start.

Google’s Recommendation Systems course description

What you will learn:

• Describe the purpose of recommendation systems.
• Understand the components of a recommendation system including candidate generation, scoring, and re-ranking.
• Use embeddings to represent items and queries.
• Develop a deeper technical understanding of common techniques used in candidate generation.
• Use TensorFlow to develop two models used for recommendation: matrix factorization and softmax.

Course content

• Background
  • Large-Scale Recommendation Systems
  • Terminology
  • Recommendation systems overview
• Candidate Generation
  • Content-based filtering
  • Collaborative filtering and matrix factorization
  • Deep neural network models
• Retrieval, scoring, re-ranking

Prerequisites

This course assumes you have:

• Completed Machine Learning Crash Course either in-person or self-study, or you have equivalent knowledge.
• Familiarity with linear algebra (inner product, matrix-vector product).
• At least a little experience programming with TensorFlow and pandas.

Course details

• Institution: Google
• Level: Intermediate
• Language: English
• Duration: 4 hours, self-paced
• Provider: Google Developers

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