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.
Project on Recommendation Engine - Advanced Book Recommender
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Project on Recommendation Engine - Advanced Book Recommender
This course is part of Mastering Recommendation Systems with Python Specialization
Instructor: EDUCBA
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4.6
(25 reviews)
Intermediate level
Recommended experience
4 hours to complete
Flexible schedule
Learn at your own pace
Skills you'll gain
Details to know
Shareable certificate
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Recently updated!
July 2025
Assessments
6 assignments
Taught in English
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This course is part of the Mastering Recommendation Systems with Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 2 modules in this course
This module introduces learners to the core structure of a personalized book recommendation system. Starting with foundational project setup, it guides through the logic of accepting user input, handling book data, and establishing a baseline model for evaluation. The module also delves into the preprocessing steps required to make user and book data machine-readable by converting identifiers into indexed forms. Learners will develop an understanding of how to construct a user-item interaction matrix and prepare the data for more advanced recommendation algorithms in future modules.
What's included
7 videos3 assignments
This module guides learners through the technical implementation of a hybrid recommendation engine by combining collaborative filtering and content-based methods. It begins with foundational data processing using Python libraries like Pandas and NumPy, and progresses toward integrating both filtering approaches into a unified hybrid model. Learners will gain hands-on experience with similarity computation, function-based model construction, and performance refinement through blending multiple data signals.
What's included
4 videos3 assignments
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Learner reviews
4.6
25 reviews
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Showing 3 of 25
OT
5
Reviewed on Aug 11, 2025
Well-designed project demonstrating advanced techniques to build an accurate and personalized book recommendation engine.
AP
4
Reviewed on Aug 30, 2025
I truly enjoyed this course! The advanced recommender project pushed my limits, yet the instructor’s guidance ensured strong understanding. Now I can design real AI solutions.
AJ
4
Reviewed on Aug 14, 2025
Effective, hands-on project for advanced book recommendation systems.
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