01Module 8 - Recommender SystemsLearn how modern recommendation engines work - from collaborative filtering and matrix factorization to neural two-tower models and learning to rank - as applied in production systems at Netflix, Amazon, and Spotify.02Collaborative Filtering - How Netflix Knows You Better Than You Know YourselfLearn how user-based and item-based collaborative filtering work from first principles - the math behind cosine similarity and Pearson correlation, how Amazon's item-to-item CF changed the industry, and how to build production-grade recommendation engines.03Content-Based Filtering - Recommending by What Items Are Made OfLearn how content-based filtering builds item feature vectors, constructs user profiles, and scores unseen items using TF-IDF and cosine similarity - no user overlap required.04Matrix Factorization - Discovering Hidden Taste DimensionsMaster matrix factorization for recommendations - SVD, Funk SVD, SGD and ALS optimization, biases, regularization, and implicit feedback with BPR. The algorithm that won the Netflix Prize.05Neural Collaborative Filtering - Beyond the Dot ProductHow deep learning revolutionized recommendations by replacing the linear dot product with learnable nonlinear interactions between users and items.06Two-Tower Models - The Architecture Powering Google, TikTok, and YouTubeHow two-tower neural networks enable billion-scale retrieval by learning separate user and item towers that can be precomputed for ultra-fast inference.07Learning to Rank - Teaching Models to Sort, Not Just ScoreHow pointwise, pairwise, and listwise ranking approaches train models to produce the optimal ordering of items for search and recommendation.08The Cold Start Problem - When Your Recommender Knows NothingHow to recommend to new users and new items when collaborative filtering has no interaction history - the cold start problem and its production solutions.
