Unlock your full potential with real-world machine learning projects
Take your machine learning journey to the next level with this hands-on, eight-week course designed to give you real-world experience in applying your skills. As the final step in our comprehensive machine learning program, this course immerses you in a collaborative project where you’ll tackle meaningful, real-world challenges using the latest machine learning techniques.
Through this course, you’ll:
- Gain extensive experience working with Python and powerful machine learning libraries like scikit-learn, Pandas, and Matplotlib.
- Apply Agile methodology to manage your project, giving you critical skills to thrive in fast-paced tech environments.
Each week, you’ll stay actively engaged with hands-on tasks, from data preparation and model selection to hyperparameter tuning and performance evaluation. You’ll work on supervised learning and dive into deep learning and algorithm optimization—all while sharpening your collaboration skills in a team-based setting.
By the end of the course, you’ll have completed a comprehensive machine learning project that showcases your ability to solve real-world problems, optimize models, and work effectively within a team. This capstone project will not only demonstrate your technical expertise but also prove your readiness to tackle the challenges of today’s evolving data science and machine learning industries.
Learning outcomes:
- Demonstrate the ability to clean, preprocess, and explore complex datasets to identify patterns, trends, and potential issues.
- Apply machine learning techniques to real-world problems.
- Implement a complete machine learning project using Python and relevant libraries, from inception to presentation.
- Critically evaluate machine learning models and their performance.
- Collaborate effectively in teams using Agile project management principles.
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Module 1: Introduction and Project Planning (Ideation) |
Get introduced to Agile project management, form project groups, and brainstorm project ideas. |
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Module 2: Project Proposal and Data Preparation |
Develop a project proposal by selecting a project idea, defining requirements, setting up a project management tool, preparing data, and creating initial visualizations. |
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Module 3: Model Selection and Baseline Implementation |
Explore model selection by reviewing supervised and unsupervised (if appllicable) learning techniques and implementing a baseline model with initial justification. |
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Module 4: Model Tuning and Optimization |
Optimize the model through hyperparameter tuning, evaluation with cross-validation, and regularization, while considering ethical implications and model bias. |
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Module 5: Project Development and Testing |
Finalize the model and project results, and prepare the final report and presentation. |
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Module 6: Project Presentation |
Present the completed project, reflect on lessons learned, and discuss future applications and learning opportunities. |
- Tailored for professionals or learners looking to solidify their machine learning skills by applying them to real-world challenges, particularly in collaborative environments.
- Designed for intermediate learners who have completed the Machine Learning Practitioner Certificate.
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.
- Get exclusive access to your program instructor—a University of Waterloo faculty expert in machine learning. Book office hours with the instructor to discuss your project progress, address challenges, and get feedback.
- Learn online and attend live sessions for project reviews and presentations.
- Attend a live orientation session before your course starts to get up to speed on the curriculum.
- Engage directly with your peers through online discussion boards.
Academic requirements
- Completion of the Machine Learning Practitioner Certificate is required prior to this course.
- Completion of Unsupervised Machine Learning course may be helpful.
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.
Receive a certificate from the University of Waterloo
Applied Machine Learning: Projects in Practice Certificate
Upon successful completion of this program, you will receive the Applied Machine Learning: Projects in Practice Certificate.
This certificate is awarded to students who have successfully applied supervised machine learning concepts in a comprehensive project. It highlights their ability to manage real-world machine learning tasks, from inception to completion, including data preparation, model tuning, and team collaboration.