Mastering Recommendation Systems with Python

Coursera MOOC / Non-credit USD 49

Enroll now →

Mastering Recommendation Systems with Python

About this course

This Specialization equips learners with practical skills to design and implement robust recommendation systems using Python. Spanning foundational techniques to hybrid models, it covers collaborative filtering, content-based filtering, and real-world deployment strategies using libraries like Surprise, Pandas, and Scikit-learn. Learners will explore use cases like movie and book recommenders, applying best practices from real-world platforms.

What you'll learn

Design recommendation systems using collaborative filtering
Implement content-based filtering techniques
Utilize libraries like Surprise, Pandas, and Scikit-learn
Deploy recommendation models in real-world applications

Skills you'll gain

python scikit-learn pandas machine learning recommendation systems data science real-world applications content-based filtering collaborative filtering surprise

Related courses

Machine Learning for Trading

Data Science Foundations

DataPrep for H2O Driverless AI

Internet of Things and AI Cloud

The Complete Quantum Computing Course

Fundamentals of Natural Language Processing

Applied Natural Language Processing in Engineering Part 2

Information Extraction from Free Text Data in Health

Course details are provided by the platform and may change — always confirm on the provider's site. Links may be affiliate links.