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Introduction to Applied Machine Learning
Intermediate English Course CertificateFREE
Our recommendation
An Amii (Alberta Machine Intelligence Institute) course on Coursera that focuses on the part of ML that decides whether a project succeeds: defining the problem and preparing the data. Rather than algorithms, it emphasises the practical judgement around framing and data readiness. Valuable for people who will lead or shape ML projects rather than just build models.
Good for: Learning to frame ML problems and prepare data — the make-or-break part.
Less suitable if: You want to learn algorithms or write model code.
Requirements: Some familiarity with ML concepts helps.
Realistic time: Around 8–12 hours.
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.
What you'll learn
Define a machine-learning problem clearly
Turn a business need into an ML application
Survey available data and spot ML opportunities
Prepare data for effective machine learning
Recognise where ML adds value — and where it doesn't
Set up a project for success before modelling
This course includes
Yes
Certificate
Yes
Mobile access
English
Language
What it costs
You can audit the course free on Coursera. A certificate needs a Coursera subscription (about $49/month) or Coursera Plus, with financial aid available.
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A Amii (Alberta Machine Intelligence Institute) certificate on Coursera is a credible learning record. It is a course certificate rather than a professional qualification or degree.
Last updated
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.