Apprivoiser l’apprentissage automatique

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The main objective of the Taming Machine Learning MOOC is to introduce you to important concepts in a simplified way and then practice them through 7 Python tutorials on the free-to-access online application, Colab. The theoretical level is adjusted to emphasize the principles of the methods presented, illustrated with concrete examples. There are few advanced mathematical demonstrations.

Why take this Machine Learning (ML) MOOC?

ML is coming to your organization soon, and you want to be prepared.
You've been using it for a while and want to stay updated.
You're considering a career shift and want to test your interest.
You aim to steer your company towards adopting artificial intelligence (AI).
You have been asked to create a team or project in AI and would like to learn enough about the subject to manage it and recruit qualified personnel.
You're simply interested in ML and AI and want to learn more.

You will be introduced to all the steps required in an ML project. Want to predict the pressure inside a turbine based on data from multiple sensors? That's regression! Want to predict whether a patient has diabetes based on medical examination results? That's classification! Want to group customers into different segments? That's clustering! There are countless applications in a multitude of fields.

To effectively apply ML in a project, one must first understand the importance of data, how to clean it to highlight its value, and then which ML method would extract the relevant information.

The course is divided into seven modules that you can follow at your own pace. You can test your understanding with feedback through a questionnaire in each module.

This MOOC is the result of a collaboration between the Data Valorization Institute (IVADO) at Université de Montréal, the Intelligence and Data Institute (IID) at Université Laval, Québec, and Mila - the Quebec Artificial Intelligence Institute.

The content was developed by professors, data scientists, computer scientists, and engineers with experience in academic and industrial R&D.

In this MOOC, the masculine gender is used as a generic term, solely to make the text lighter.