Build a practical movie recommendation system using Python through a complete, hands-on workflow. You’ll begin by exploring recommendation system concepts, real-world applications, and the fundamentals of collaborative filtering. You’ll then configure your Python environment with Anaconda and the Surprise library, prepare real user data, and develop a predictive model that generates personalized movie recommendations.

Designed for learners interested in Python, machine learning, and recommendation engines, this course takes you from core concepts to implementation. You’ll learn to analyze datasets, build and validate a collaborative filtering model, evaluate its performance through cross-validation using RMSE and MAE, interpret prediction results, and create structured Python functions that produce top movie predictions. What makes this course distinctive is its focused, end-to-end approach: every concept supports the creation of a working recommendation model. By the end, you’ll be able to prepare recommendation datasets, implement and assess predictive models, and generate personalized movie suggestions using a reproducible Python workflow. Enroll to gain practical experience building a recommendation engine from scratch and applying machine learning techniques to real user data.