Design and implement a practical Book Recommendation Engine with Python in this hands-on, project-based course. You’ll explore the objectives, scope, and architecture of a book recommender system before preparing structured data through preprocessing and reusable utility functions.

As you progress, you’ll engineer publication metadata to support user-defined filtering based on book information and preferences. You’ll then build a content-based recommendation model using text preprocessing, TF-IDF, Count Vectorizers, similarity scoring, and similarity matrices. By combining and transforming features such as book title, author, genre, and description through the soup method, you’ll learn to improve recommendation relevance and produce more personalized results. This course is designed for learners seeking practical experience with Python, data science, content-based filtering, and recommender systems. By the end, you’ll be able to preprocess book datasets, extract and transform metadata, construct filtering and similarity frameworks, combine text-based features, and refine recommendation outputs. What makes this course distinctive is its focused, end-to-end book recommendation project, connecting foundational concepts directly to implementation. Enroll to gain practical experience designing a functional recommendation engine using structured and textual book data.