Recommendation Systems Courses

Recommendation systems analyze user behavior and item attributes to suggest relevant content. Common approaches include collaborative filtering (similar users), content-based filtering (similar items), and hybrid methods combining both.

You need Python programming, linear algebra, and basic ML knowledge. Understanding of matrix factorization, embeddings, and evaluation metrics like NDCG and MAP is important for production systems.

Netflix, Amazon, Spotify, YouTube, TikTok, and virtually every major platform uses recommendation engines. They drive up to 80% of content consumption on streaming platforms.

Deep learning enables neural collaborative filtering, sequence-aware recommendations using transformers, and multi-modal recommendations that combine text, images, and user behavior signals.