Build a movie recommendation system using Python and real-world movie data in this hands-on, project-based course. You’ll explore how recommender systems support modern digital platforms while creating both popularity-based and content-based movie recommendation models.

You’ll begin with the fundamentals of recommendation systems, set up your Python development environment, import essential libraries, and develop a basic recommendation engine using popularity metrics. You’ll then advance to content-based filtering by preprocessing movie data, extracting meaningful metadata, engineering textual features, and analyzing similarities to generate personalized movie recommendations. Designed for data enthusiasts and aspiring machine learning developers, this course combines core concepts with practical coding. By the end, you’ll be able to construct and evaluate recommender models, apply data preprocessing and feature engineering techniques, and explain how popularity-based and content-based recommendation engines work. What makes this course distinctive is its focused, end-to-end project structure. Every lesson moves you from foundational concepts to a working movie recommender system, giving you practical Python experience with real-world data and a solid foundation in recommendation systems.