Build ML-Powered Recommender Systems with Python
Learn to create intelligent recommendation systems using Python and machine learning. Master content-based and collaborative filtering techniques.
Learn to create intelligent recommendation systems using Python and machine learning. Master content-based and collaborative filtering techniques.
This comprehensive course teaches you to build sophisticated recommender systems using Python and machine learning. Starting with fundamental concepts and taxonomies, you'll progress to implementing both content-based and collaborative filtering techniques. The curriculum covers essential topics like AI integration, data evaluation, and practical implementation using tools like tf-idf and KNN. Through hands-on projects in song and movie recommendations, students gain real-world experience in building and evaluating recommendation engines.
Instructors:
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What you'll learn
Understand the basics of AI-integrated recommender systems
Analyze the impact of overfitting, underfitting, bias, and variance
Apply machine learning and Python to build content-based recommender systems
Create and model a KNN-based recommender engine
Implement collaborative filtering techniques
Evaluate recommendation system performance
Skills you'll gain
This course includes:
374 Minutes PreRecorded video
3 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 6 modules in this course
This course provides a comprehensive introduction to building recommender systems using machine learning and Python. Students learn both theoretical foundations and practical implementation techniques, covering essential concepts like content-based and collaborative filtering. The curriculum includes hands-on projects in building song and movie recommendation systems, teaching students to evaluate datasets, implement machine learning algorithms, and create effective recommendation engines. Special emphasis is placed on understanding AI integration, system evaluation, and real-world applications.
Introduction
Module 1 · 19 Minutes to complete
Motivation for Recommender System
Module 2 · 27 Minutes to complete
Basic of Recommender Systems
Module 3 · 1 Hours to complete
Machine Learning for Recommender System
Module 4 · 2 Hours to complete
Project 1: Song Recommendation System Using Content-Based Filtering
Module 5 · 51 Minutes to complete
Project 2: Movie Recommendation System Using Collaborative Filtering
Module 6 · 2 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Enhancing IT Education Through Expert-Led Learning
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