Duration
6 weeksWeekly study
3 hours100% online
How it works
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.
Syllabus
Week 1
Recommender systems and their applications
Introduction to Recommender Systems
Define a recommender system and identify why we need it.
Recommendation Approaches
Identify different recommendation approaches.
Recommender Implementation and Evaluation
Identify steps of building a recommender. Recognize how to evaluate a recommender.
Python Practice
Install Python development environment and run Python programs.
Datasets
Explore what a dataset is and why it is important to build a recommender system.
Lecture Notes and Source Code
Download lecture notes and source code and explore them.
Week 2
Fundamental Recommenders
Data Collection
Collect the data that we use to build a recommender. Look into the details of the data items.
Data Organization and Metrics
Organize and prepare data for a recommender. Identify and design metrics for recommenders.
A Recommender based on Certain Metrics
Build a recommender based on a certain metric.
A Recommender based on User’s Preferences
Build a recommender based on user’s preferences.
A Recommender based on Similarities
Build a recommender based on similarities.
Week 3
Content-based Recommender
Content-based Filtering
Explore content-based filtering. Explore the dataset used to illustrate the content-based filtering.
A Content-based Recommender
Explore the dataset again for a recommender. Design metrics for a recommender.
TF-IDF for a Recommender
Calculate TF-IDF for a recommender.
A Content-based Recommender using TF-IDF
Calculate cosine similarity for a recommender. Build a content-based recommender using TF-IDF.
Week 4
Collaborative Filtering Recommender
Collaborative Filtering
Explore collaborative filtering
A User-Based CF Recommender
Build a user-based collaborative filtering recommender
An Item-based CF Recommender
Build a item-based collaborative filtering (CF) recommender
Matrix Factorization
Explore matrix factorization and its role in collaborative filtering (CF) recommenders.
A Model-Based CF Recommender
Build a model-based collaborative filtering recommender
Week 5
Artificial Intelligence (AI) and Machine Learning (ML)
AI, Machine and Deep Learning
Explore AI, machine learning, and deep learning.
Machine Learning: Regression
Use linear regression for prediction tasks.
Machine Learning: K-Means
Use K-means to cluster data points.
Machine Learning: K-Nearest Neighbors (KNN)
Use K-Nearest Neighbors (KNN) to classify data points.
Deep Learning
Build a deep learning application.
Week 6
Machine Learning Recommender
Recommenders using Machine Learning
Explore recommenders using machine learning.
A Recommender using Linear Regression
Build a recommender using linear regression.
A Recommender using K-means
Build a recommender using K-means.
A Recommender using K-Nearest Neighbors (KNN)
Build a recommender using K-Nearest Neighbors (KNN)
A Recommender using Deep Learning
Build a recommender using Neural Networks.
When would you like to start?
Start straight away and join a global classroom of learners. If the course hasn’t started yet you’ll see the future date listed below.
Available now
Learning on this course
On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.
What will you achieve?
By the end of the course, you‘ll be able to...
- 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.
Who is the course for?
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.
Who will you learn with?
Chin-Chih Chang is an assistant professor in the Dept. of Comp. Sci. and Info. Eng., Chung Hua University, Hsinchu, Taiwan. His research interests include deep learning, recommender systems, etc.
Ways to learn | Buy this course | Subscribe & save | Limited access |
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Choose the best way to learn for you! | $109/one-off payment | $244.99 for a whole year Automatically renews | Free |
Fulfill your current learning need | Develop skills to further your career | Sample the course materials | |
Access to this course | tick | tick | Access expires 26 Mar 2025 |
Access to 1,000+ courses | cross | tick | cross |
Learn at your own pace | tick | tick | cross |
Discuss your learning in comments | tick | tick | tick |
Tests to check your learning | tick | tick | cross |
Certificate when you're eligible | Printed and digital | Digital only | cross |
Cancel for free anytime |
Ways to learn
Choose the best way to learn for you!
Subscribe & save
$244.99 for a whole year
Automatically renews
Develop skills to further your career
- Access to this course
- Access to 1,000+ courses
- Learn at your own pace
- Discuss your learning in comments
- Tests to boost your learning
- Digital certificate when you're eligible
Cancel for free anytime
Buy this course
$109/one-off payment
Fulfill your current learning need
- Access to this course
- Learn at your own pace
- Discuss your learning in comments
- Tests to boost your learning
- Printed and digital certificate when you’re eligible
Limited access
Free
Sample the course materials
- Access expires 26 Mar 2025
Find out more about certificates, Unlimited or buying a course (Upgrades) Sale price available until 3 March 2025 at 23:59 (UTC). T&Cs apply. |
Find out more about certificates, Unlimited or buying a course (Upgrades)
Sale price available until 3 March 2025 at 23:59 (UTC). T&Cs apply.
Learning on FutureLearn
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Join a global classroom
- Experience the power of social learning, and get inspired by an international network of learners
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Map your progress
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- Whenever you’re ready, mark each step as complete, you’re in control
- Complete 90% of course steps and all of the assessments to earn your certificate
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