Coursera MOOC / Non-credit 0

Project on Recommendation Engine - Advanced Book Recommender

Name: Project on Recommendation Engine - Advanced Book Recommender
Price: 49 USD
Availability: InStock

About this course

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.

68/100

CourseAsk score

What the provider tells you: 32/45
Who stands behind it: 20/35
How complete the listing is: 16/20

Scores how much the provider publishes and who stands behind it — not how well it is taught.

What you'll learn

create a hybrid book recommendation system
preprocess data with Pandas and NumPy
compute similarities between items
implement collaborative and content-based filtering
build a user-item interaction matrix

Machine Learning #python #pandas #machine learning #numpy #recommendation systems #scalable systems #data preprocessing #content-based filtering #collaborative filtering #user interactions

$49.00

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