Transition from theory to application by learning about core machine learning concepts and their application in the low-carbon energy sector.

In this course, you’ll learn to identify high-impact ML opportunities, navigate the complexities of energy data, and understand how different machine learning models work. Adding to your coding experience, you’ll learn to evaluate model performance using industry-specific metrics and apply essential governance principles to ensure your solutions are responsible, transparent, and regulatory-compliant.

Building a model is only half the battle; the real value is found in deployment.

This course bridges the gap between a successful experiment and an industry-ready solution. You’ll explore common deployment patterns and evaluate the trade-offs between cloud and on-premise architectures. By the end of this module, you’ll have the strategic roadmap and technical insight needed to scale your ML applications into a production-grade environment, fully preparing you for your hands-on capstone project.

Prove your knowledge by developing a production-ready solution for a challenge in the low-carbon energy sector. This Capstone Project challenges you to apply your knowledge, tasking you with navigating real-world data and deployment constraints to deliver a scalable, governance-compliant, and industry-validated final project.

• Challenge: You’ll be given vetted, high-fidelity data sets and tasked with executing the technical development work to answer a critical machine learning question. These data sets are provided by subject matter experts from the University of Alberta’s Faculty of Engineering.

• Technical Coaching: Engage in office hours and technical check-ins with Amii’s Machine Learning Educators to refine your code and approach.

• Deliverable: Provide a completed, functional codebase that proves your ability to work with real-world data, from preparation and feature engineering to model evaluation and deployment.

Completion of Technical Track is recognized through a blockchain-verified digital badge. Unlike a simple certificate, this badge attaches your actual capstone project, allowing you to share your work and prove your skills to current and future employers on LinkedIn.

Foundations Track
You can plan for an estimated 3 hours per course (there are 4 foundational, asynchronous courses in total) to complete the program. Plus, you’ll find optional but highly valuable weekly assets, ranging from quick, focused 15-minute reads to more in-depth assets up to 2 hours in length. Since the training is self-paced, you can work through this material whenever it suits you best.

Technical Track
You can plan for an estimated ~20 hours for all three courses to complete Technical Track.

This flexible, self-paced format allows you to access the materials anytime, anywhere, so you can fit your professional development around your schedule.

Your learning journey begins soon after approval! Once your application is successfully approved, you will be notified of your specific access date via email. This allows us to ensure everything is ready to go for a smooth start to your program.

To qualify for the scholarship, applicants must be currently employed within Canada’s energy sector. They must be legally entitled to work in Canada as a Canadian citizen, permanent resident, or individual with refugee status. They must also agree to the program’s terms and requirements, including providing consent to share information with the Government of Canada for evaluation and reporting purposes.

The scholarship process operates on a rolling basis. Eligible applicants are reviewed as submissions are received, and selections are made until we fill our seats. We strongly encourage you to apply as soon as possible to secure your spot.

If accepted, you'll receive a decision within ten business days. We will communicate your assigned training batch and start date well in advance to allow for proper planning.

We offer two distinct paths tailored for energy professionals. The Foundations Track is designed to build essential AI literacy, while the Technical Track takes things to the next level and walks you through how to train and deploy a model if the Canadian Low Carbon Energy context. Please note that the Technical Track requires you to be proficient in Python coding,

Yes! You may apply for both Foundations Track and Technical Track, provided you meet the eligibility requirements for each program. Foundations Track is not a prerequisite for Technical Track, and eligible individuals may enroll in either or both tracks.