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 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.

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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