Build a personalized hybrid book recommendation system using Python by combining collaborative filtering and content-based recommendation techniques. In this project-based course, you’ll develop a complete recommendation pipeline that turns user interactions and book data into meaningful, user-focused recommendations.

You’ll begin with project setup, user input handling, and baseline model evaluation. You’ll then convert raw user and book identifiers into indexed numerical formats and construct a user-item interaction matrix. Using Pandas and NumPy, you’ll preprocess data, compute similarities, and build functions that integrate collaborative and content-based filtering into a unified hybrid recommender system. This course is designed for learners seeking practical experience with Python and recommendation systems through structured coding exercises, quizzes, and hands-on implementation. By the end, you’ll be able to prepare recommendation data, implement hybrid filtering logic, and build a scalable Python-based book recommendation system for user-centric applications. What makes this course distinctive is its focused progression from foundational data preparation to a functional hybrid model. Enroll to understand how multiple recommendation strategies work together and apply that knowledge in a practical book recommendation project.