This course empowers learners to design, develop, and evaluate movie recommendation systems using real-world data and Python programming. Tailored for data enthusiasts and aspiring machine learning developers, the course introduces the practical applications of recommender systems across modern digital platforms such as Netflix, Amazon, and YouTube.

Beginning with foundational concepts, learners will set up their Python environment and build a simple recommender based on popularity metrics. As the course progresses, learners will transition to constructing a more nuanced content-based recommender, utilizing rich metadata such as genres, keywords, and cast to provide personalized recommendations. By completing this course, learners will gain hands-on experience in preprocessing data, engineering features, and applying core machine learning techniques for real-time decision-making. The instruction is aligned with Bloom’s Taxonomy, guiding learners to construct, analyze, and apply recommender models effectively.