Introduction
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Estimated course time: 4 hoursWelcome to Recommendation Systems ! We've designed this course
to expand your knowledge of recommendation systems and explain
different models used in recommendation, including matrix
factorization and deep neural networks.
Objectives:
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.
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).
Happy Learning!
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Last updated 2025-08-25 UTC.
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