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Abstract cover artwork for Recommender Systems from Scratch, a Machine Learning course Machine Learning
Machine Learning intermediate 15 hours 4.9

Recommender Systems from Scratch

Taught by Hiroshi Tanaka

Build a recommender end to end: implicit-feedback matrix factorisation, content-based retrieval with embeddings, two-stage retrieve-then-rank architectures, and the cold-start problem every real system faces. Evaluated with offline ranking metrics and an honest discussion of why they disagree with A/B tests.

What you'll cover

recommenders collaborative filtering embeddings ranking cold start

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