Recommender Systems in Python

Build a recommender system with National Tsing Hua University

If you’ve ever watched a recommended film on Netflix or listened to a suggested playlist on Spotify, you have used a recommender system.

On this six-week course from National Tsing Hua University, you’ll learn why so many platforms incorporate recommender systems, and how you can use Python to build your own.

Learn what recommender systems are and why so many platforms are using them

Recommender systems use complex data sets and machine learning to bring you tailored recommendations for your consumption.

The course will start with an introduction to the concept and influence of recommender systems, reviewing some of the most popular models and explaining why they have become so popular among big tech platforms.

Explore different approaches to building a recommender system

Once you’ve understood the concept and influence of recommender systems, you’ll get stuck in analysing different approaches to building them.

In Weeks 2, 3, and 4 of the course, you’ll learn how to build a recommender system in Python, using each of a variety of different approaches.

Discover the role of AI in developing recommender systems

The last three weeks of the course will explore the role AI and machine learning play in developing and enhancing recommender systems.

You’ll learn how algorithmic data can be used to make more sophisticated recommendations.

By the end of the course, you’ll have the expertise and programming skills you need to start building your first recommender system.

This course is designed for computer programmers interested in learning more about recommender systems and how to build them in Python.

Learners will need a basic understanding of computer programming to get the most out of this course.

This course is designed for computer programmers interested in learning more about recommender systems and how to build them in Python.

Learners will need a basic understanding of computer programming to get the most out of this course.

• Enhanced learning, personalized recommendations, improved engagement, adaptive skills development, and a competitive edge in articulating achievements to potential employers.
• Comprehensive user data, refined recommendations, improved personalization, enhanced user experience, and a competitive advantage in offering tailored content, fostering engagement, and articulating individual achievements effectively.
• Efficient algorithms, accurate predictions, enhanced user experience, improved engagement, and personalized learning journeys, leading to adaptive skill development and a competitive advantage in articulating achievements.
• Informed decision-making, refined suggestions, improved personalization, enhanced user experience, and a competitive advantage in offering tailored content, fostering engagement, and articulating individual achievements effectively.
• Precision in recommendations, optimized user experience, increased engagement, and a personalized learning journey, resulting in adaptive skill development and a competitive edge in articulating achievements.