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Recommendation Systems: From Basic to Production

Comprehensive course on Recommendation Systems — from Collaborative Filtering, Content-based, Matrix Factorization to Deep Learning RecSys with Two-Tower, Graph Neural Networks, Sequence Models. Hands-on with Python, PyTorch, LightFM, and deploy production-ready recommendation engine.

Part 1: Recommendation Systems Platform

Lesson 1: What are Recommendation Systems? — Overview & Taxonomy90 minLesson 2: Collaborative Filtering — User-based & Item-based120 minLesson 3: Matrix Factorization — SVD, ALS & BPR150 min

Part 2: Deep Learning for Recommendation

Lesson 4: Content-based & Feature Engineering for RecSys120 minLesson 5: Neural Collaborative Filtering & Embedding150 minLesson 6: Two-Tower Architecture & Retrieval150 minLesson 7: Sequence Models — GRU4Rec, SASRec & Transformers150 min

Part 3: Advanced RecSys — Knowledge Graph, Multi-task & GNN

Lesson 8: Graph Neural Networks for Recommendation150 minLesson 9: Knowledge Graph Recommendation120 minLesson 10: Multi-task & Multi-objective Ranking150 min

Part 4: RecSys Production — Deploy & Scale

Lesson 11: LLM-powered Recommendations150 minLesson 12: Production RecSys Architecture150 minLesson 13: A/B Testing & Evaluation for RecSys120 minLesson 14: Capstone — E-commerce Recommendation Engine240 min