This project-based course equips learners with the skills to design, develop, and implement a personalized book recommendation system using Python. Spanning two core modules, the course introduces foundational concepts of collaborative and content-based filtering and builds toward a functional hybrid model. Learners will begin by analyzing user data, constructing user-item interaction matrices, and evaluating baseline models. They will then apply advanced data handling techniques using libraries like Pandas and NumPy, and integrate multiple recommendation strategies into a single hybrid engine.

Through practical lessons, coding exercises, and quizzes, learners will progressively apply machine learning logic, synthesize similarity computations, and construct real-world recommendation systems that combine user behavior with item features. By the end of the course, learners will be able to confidently build scalable recommendation pipelines tailored for dynamic, user-centric applications.