Are you curious about how AI models like ChatGPT, Gemini, and Claude work? Maybe how algorithms in media platforms like Facebook, Instagram, TikTok, and Netflix are predicting what you want to see? What about how online vendors like Amazon determine what you want to buy and how to get it to you quickly?
Our introductory machine learning course will cover the foundations behind these powerful AI systems, how they are designed and built, and how data can be mobilized to make predictions and drive outcomes.
Introduction to Machine Learning is a short online course from the Canadian AI Advancement Institute that introduces machine learning for AI-curious learners who want to learn the basics. Ideal learners also include managers, analysts, educators and business owners who keep encountering machine learning in decisions and want to stop nodding along.
The course is delivered as a single section of approximately 2.75 hours, plus a short introduction and conclusion. It covers the types of machine learning – supervised, unsupervised, semi-supervised and reinforcement learning – the basics of how they function, as well as their applications. It also discusses the importance of data: its different types, why quality and preprocessing matter more than most people expect, and how both feed directly into model performance.
The materials consist of a mix of chapter readings from the Introduction to Machine Learning Manual, course videos, and online discussions. You complete readings and videos at your own discretion, so there is nothing live to attend and no session to miss.
Understanding the basics of machine learning is becoming necessary for decision-makers. If your organization is buying machine learning from a vendor, someone on your side has to be able to read the proposal. The Future Skills Centre found that only 31% of small and medium-sized organizations could clearly articulate the specific AI skills they needed, which is a hard position from which to judge what you are being sold.
In addition, another recent study found that in businesses with 100 or more employees using AI, 32.8% hired people with AI-related skills and 30.2% brought in external consultants or vendors. These figures show a growing need for a shared language and understanding.
There is a personal case too. The same research reports that demand for core AI skills rose 37% between 2018 and 2023, and workers with AI competencies command up to a 56% wage premium. Understanding how models learn, and what makes them fail, is the part of that skill set you can pick up in an afternoon.
CAIAI has taught AI fundamentals to people who are not engineers and don’t intend to be: property maintenance and land management teams, chamber of commerce members, executives in manufacturing and agriculture, nonprofit staff, and university faculty and students.
Machine learning is usually the part where the room goes quiet, because it is the concept most often used to sell things and least often explained. Below are just a few of the AI learning engagements we have facilitated:
These articles cover the same ground the course does, and give a fair preview of how it is taught.
Learners or employers are usually unaware of opportunities to pay for their learning. We have shared resources to help you identify available funding opportunities, including scholarships for individuals and grant programs for individuals and employers.
Apply for a scholarship through our Canadian AI scholarships page. In addition, visit our Canadian AI grant funding page to see what individuals or employers can apply for.
You can enrol directly on our learning platform, or use the form below if you want to ask any additional questions.
Our mission is to help Canadians learn, adapt and succeed with AI. Machine learning is where a lot of people stop, because it is presented as something only engineers can hold. It is not. We build our courses with industry experts, organizations and institutions so that the explanation you get is accurate and still usable by someone who has a different job to do.
