Supervised machine learning is one of the most commonly used machine learning paradigms, as it can be used to leverage existing datasets to draw outcomes that support data classification and predictions. For those who want to stay ahead, innovate, and make more informed recommendations at work, supervised learning skills are key for tackling a broad spectrum of machine learning projects.
This eight-week course uses the programming language Python to provide you with a comprehensive introduction to supervised learning without overwhelming technicalities. You will gain practical, hands-on experience properly implementing and evaluating supervised learning algorithms to draw relevant insights and solve problems.
During the course, you will explore the supervised learning tasks that are used to classify and group data or predict outcomes in real-world situations. Upon successful completion of the program, you will have an expanded machine learning portfolio that you can use to demonstrate your skills for potential employers, and foundational knowledge of the most popular and common supervised machine learning techniques.
- Individuals in non-technical roles with basic programming and data preparation knowledge, aiming to gain practical, in-demand machine learning and AI skills to stay competitive in the evolving workforce.
- 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.
- Programmers and developers looking to 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.
- Identify when to use different supervised learning tools and techniques to solve real-world problems with data categorization and predictions.
- Use regression methods to analyze labelled data and make predictions.
- Sort data into groups using classification methods.
- Measure how well your classification models work using different techniques and metrics to draw effective insights.
- Improve your models by selecting the most important variables using feature selection methods.
- 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.
- Attend a live orientation session before your course starts to get up to speed on the curriculum.
- 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, 8 - 8:30 p.m. ET
- Engage directly with your classmates through online discussion boards.
- Approximately five hours of your time each week.
Academic requirements
- Basic coding skills in any programming language are required, or completion of Python for Machine Learning: The Essential Starter Kit (or equivalent knowledge).
- Basic knowledge of statistics may be helpful but is 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.
This course forms part of a comprehensive, Machine Learning Practitioner Certificate.
Mehrdad Pirnia
Continuing Lecturer, Management Sciences, Faculty of Engineering | Program Author
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.
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