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FutureLearn

Recommender Systems in Python

National Tsing Hua University via FutureLearn

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Overview

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.

Syllabus

  • Recommender systems and their applications
    • Introduction to Recommender Systems
    • Recommendation Approaches
    • Recommender Implementation and Evaluation
    • Python Practice
    • Datasets
    • Lecture Notes and Source Code
  • Fundamental Recommenders
    • Data Collection
    • Data Organization and Metrics
    • A Recommender based on Certain Metrics
    • A Recommender based on User’s Preferences
    • A Recommender based on Similarities
  • Content-based Recommender
    • Content-based Filtering
    • A Content-based Recommender
    • TF-IDF for a Recommender
    • A Content-based Recommender using TF-IDF
  • Collaborative Filtering Recommender
    • Collaborative Filtering
    • A User-Based CF Recommender
    • An Item-based CF Recommender
    • Matrix Factorization
    • A Model-Based CF Recommender
  • Artificial Intelligence (AI) and Machine Learning (ML)
    • AI, Machine and Deep Learning
    • Machine Learning: Regression
    • Machine Learning: K-Means
    • Machine Learning: K-Nearest Neighbors (KNN)
    • Deep Learning
  • Machine Learning Recommender
    • Recommenders using Machine Learning
    • A Recommender using Linear Regression
    • A Recommender using K-means
    • A Recommender using K-Nearest Neighbors (KNN)
    • A Recommender using Deep Learning

Taught by

Tonny Menglun Kuo

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