Learn how to build a movie recommendation system using Python through a practical, end-to-end workflow. In this hands-on course, you'll explore the fundamentals of recommendation systems and collaborative filtering before preparing datasets and configuring your Python environment with Anaconda and the Surprise library. You'll then build, validate, and apply a recommendation model that generates personalized movie predictions using real user data.

Designed for learners interested in Python, machine learning, and recommendation systems, this course emphasizes practical implementation at every stage. You'll work with datasets, construct predictive models, evaluate performance using cross-validation with RMSE and MAE, and write Python functions to generate accurate movie recommendations. Along the way, you'll gain experience interpreting prediction results and implementing reproducible machine learning workflows. What makes this course unique is its complete, hands-on approach—from understanding recommendation engine concepts to deploying a working prediction system. By the end of the course, you'll be able to analyze datasets, implement collaborative filtering algorithms, validate model performance, and create personalized movie recommendation features using Python.