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 learn how to design, develop, and implement a recommendation pipeline that transforms user interactions and book data into meaningful recommendations.

You'll begin by building a strong foundation, including project setup, user input handling, user and book indexing, and constructing a user-item interaction matrix for baseline model evaluation. Next, you'll preprocess data using Pandas and NumPy, compute similarities, and integrate collaborative and content-based filtering into a functional hybrid recommendation model. This course is designed for learners who want practical experience building recommendation systems through structured coding exercises, quizzes, and hands-on implementation. By progressing from foundational data preparation to hybrid model construction, you'll gain a clear understanding of how multiple recommendation strategies work together. By the end of the course, 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.