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Alberta Machine Intelligence Institute · on Coursera

Introduction to Applied Machine Learning

Intermediate English Course CertificateFREE
SkillsApplied machine learningProblem framingData preparationML project planningData readiness

Is this course right for you?

Our take
An Amii course that zeroes in on the part of machine learning that quietly decides whether a project lives or dies: framing the problem and getting the data ready.

Good for: Learning to frame ML problems and prepare data — the make-or-break part.

Skip if: You want to learn algorithms or write model code.

Instead of algorithms, it builds the practical judgement around problem definition and data readiness — the work that happens before any model is trained and that most technical courses skip. That makes it genuinely valuable for people who'll lead or shape ML projects, and a mismatch if what you actually want is to learn algorithms or write model code.

Free to audit on Coursera; the certificate needs a subscription (about $49/month) or Coursera Plus, with financial aid available, and marks the study rather than a credential. The judgement it teaches about framing and data doesn't date (as of 2026).

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About this course

Introduction to Applied Machine Learning focuses on the part of ML that decides whether a project succeeds: defining the problem and preparing the data. From Amii (the Alberta Machine Intelligence Institute) and instructor Anna Koop, it teaches how to take a real business need — in finance, medicine, engineering, or elsewhere — and turn it into a well-framed machine-learning application.

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Instructor

AK
Anna Koop
Coursera instructor

Anna Koop teaches for the Alberta Machine Intelligence Institute (Amii), one of Canada's leading AI research institutes. The course reflects Amii's applied focus — getting ML to work on real problems, not just algorithms in isolation.

Frequently asked questions

Not in depth mathematically. From the Alberta Machine Intelligence Institute (Amii), it focuses on how to apply machine learning to real problems — framing problems, understanding what ML can and cannot do, and the practical workflow — rather than deriving algorithms or heavy coding. It gives you the applied and strategic side of ML, so you understand how to use it, without the mathematical depth of a technical algorithms course.

No. It is designed as an applied introduction, assuming no prior machine-learning knowledge, and it avoids heavy maths and coding. Some general comfort with data and business problems helps, since it is about applying ML sensibly, but it is genuinely pitched at newcomers and non-specialists wanting to understand how machine learning fits into solving real problems.

Managers, analysts, and professionals who need to understand and apply machine learning — deciding where it fits, working with technical teams, and judging ML projects — rather than build models themselves. It suits people wanting the practical, decision-making perspective on ML from a respected AI institute, as opposed to engineers seeking hands-on coding, who would take a technical course instead.

Yes, you can audit it on Coursera and watch the lectures at no cost. The graded elements and the certificate require a subscription, with financial aid available if you apply. Since much of the value is understanding how to apply ML, auditing gives you the substance if you want the concepts rather than the credential.

It typically runs a few weeks at a modest weekly pace as a focused course. It is concept- and application-driven rather than a hands-on coding build, so the time goes into understanding how machine learning is applied to real problems, with any wider Amii offerings a longer commitment if you continue.
Free
to audit
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