Unsupervised machine learning is a powerful tool for extracting meaningful information from unlabelled data. Unlike supervised learning, where the algorithm is provided with labelled examples to learn from, unsupervised learning enables you to find patterns, relationships, and structures within data without explicit guidance. It also helps address a wider variety of challenges, contributing to more thorough analysis of complex data sets.
This course covers three key concepts and practices of unsupervised machine learning—dimensionality reduction, association rules, and clustering. Through independent study, you will learn how these three unsupervised learning techniques can be used to discover patterns and similarities in unlabelled data sets without human intervention. You will also gain hands-on experience using Python to prepare and process data for unsupervised learning tasks, implement principle component analysis, and interpret clusters using hierarchical and k-mean clustering.
Upon successful completion of the course, you will have an enhanced machine learning
portfolio to show potential employers and be equipped with the skills needed to apply
common unsupervised learning techniques in practical ways.
- Current or aspiring data analysts or data scientists looking to build a machine learning portfolio.
- Data professionals looking to add machine learning techniques to their domain.
- Individuals with basic knowledge in programming and mathematics who want to expand their machine learning knowledge and skills.
Learn alongside your team!
WatSPEED provides custom learning experiences tailored for large groups from any single organization. Register three or more employees from the same organization and receive 15 per cent off. Contact our team at watspeed@uwaterloo.ca for details.
- Understand and explain the principles and significance of unsupervised machine learning.
- Reduce the number of variables or features in large data sets using dimensionality reduction techniques.
- Analyze data for patterns or co-occurrences using association rule techniques.
- Discover hidden patterns or clusters within unlabelled data sets using clustering techniques.
- Exclusive access to your program author—a University of Waterloo faculty expert in machine learning.
- Learn at your own pace with weekly independent online learning and hands-on exercises and come away with a machine learning portfolio that demonstrates your skills.
- Optional live drop-in sessions twice weekly via Zoom where you can ask questions and
receive instructor support on key course concepts:
- Wednesdays, 2 - 2:30 p.m. ET
- Wednesdays, 6 - 6:30 p.m. ET
- Engage directly with your classmates through online discussion boards.
- Approximately five hours of your time each week.
Academic requirements
- It is recommended that you take Python for Machine Learning: The Essential Starter Kit first or possess equivalent prior knowledge using Python to code and prepare data.
- Post-secondary level statistics and calculus may be helpful but are not required.
System requirements
- Jupyter Notebook/Anaconda (software that you are required to install)
Receive a certificate from the University of Waterloo
Upon successful completion of this program, you will receive a professional education certificate from the University of Waterloo.
Mehrdad Pirnia
Continuing Lecturer, Management Sciences, Faculty of Engineering | Program Author and Instructor
Dr. Mehrdad Pirnia is a faculty member at the University of Waterloo, Department of Management Sciences. Before joining Waterloo, he worked full-time in California ISO and ALSTOM Grid. He also did an internship at Federal Energy Regulatory Commission (FERC) during his PhD program.
Dr. Pirnia received his Ph.D. degree from University of Waterloo in 2014 in Electrical and Computer Engineering (Power Systems Optimization). The focus of his research is on applying AI, optimization, and stochastic techniques to enhance the operation and planning of energy systems.
Manda (Hongxiu) Li
Senior Machine Learning Engineer, Block | Program Contributor
Dr. Manda Li is an accomplished machine learning professional and researcher with a profound passion for machine learning applications and statistical analysis. She obtained her PhD in Economics from the University of Waterloo in 2017, focusing her research on climate change, innovation, and empirical modeling. With a robust background in statistics, modeling, and machine learning, Manda has extensive industry experience in the finance, insurance, and banking sectors. This practical expertise enables her to bridge real-world challenges with the content covered in the machine learning courses, providing students with valuable insights into the practical applications of their studies.