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Recommendation Systems

Recommendation Systems is a Google for Developers course on the models behind recommendations, including matrix factorisation and deep neural networks, and on the components of a recommender: candidate generation, scoring and re-ranking.

Open at Google ↗Common questions

Who it suits

  • Engineers building feeds, search or product recommendations.
  • ML practitioners who have finished Machine Learning Crash Course.

What it covers

  • The purpose of recommendation systems.
  • The components: candidate generation, scoring and re-ranking.
  • Using embeddings to represent items and queries.
  • Common techniques used in candidate generation.

Before you start

  • Completion of Machine Learning Crash Course, or equivalent knowledge.
  • Familiarity with linear algebra, such as the inner product and matrix-vector product.

Common questions about Recommendation Systems

How long does it take?
Google estimates 4 hours.
Do I need Crash Course first?
Google says to complete it in person or by self-study, or have equivalent knowledge.
Is it free to reuse?
Google says the content is licensed under Creative Commons Attribution 4.0, and code samples under Apache 2.0.

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Where this comes from

Written from Google’s own pages, read on 2026-10-14. Content, prices and availability change, so the provider’s page is the source. We are not affiliated with Google and this is not an endorsement.