This document provides an overview of recommendation systems and includes hands-on practices using different recommendation techniques. It discusses use cases for recommendation systems, different recommendation types including content-based, collaborative filtering using user-based and item-based approaches, and model-based recommendation using LightFM and neural collaborative filtering (NCF). Code examples are provided to demonstrate baseline, user-based and item-based collaborative filtering, content-based filtering using TF-IDF, and models using LightFM and NCF. Evaluation metrics like MRR and confusion matrix are also discussed.