Learn how to design, build, and evaluate a movie recommendation system using Python and real-world movie data. In this hands-on course, you'll explore how recommender systems power popular digital platforms by creating both popularity-based and content-based movie recommendation models.

You'll begin by understanding the fundamentals of recommendation systems, setting up your Python development environment, and building a recommendation engine based on popularity metrics. As you progress, you'll develop a content-based recommender 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 practical coding with core machine learning concepts to help you understand how recommendation engines work in real-world applications. Throughout the course, you'll construct, analyze, and evaluate recommender models while strengthening your ability to apply data preprocessing and feature engineering techniques. If you want practical experience building recommendation systems with Python and gain a solid foundation in content-based filtering using real-world datasets, this course provides a structured, project-based learning experience.