A scikit-learn MOOC developed by its creators!

18 May. to 14 Jul. 2021
Changed on 12/05/2021
More than 2,000 contributors worldwide, 70 million visits to the site in 2020, ranking as the third most used free machine learning software in the world, Inria-Academy of Science Innovation Award 2019: scikit-learn is a success story, attested to by the existence of a consortium of user companies that finance its development. From 18 May to 14 July, the creators and developers of Scikit-learn, the reference software library in machine learning, are offering their MOOC in English, completely free of charge, to learn in concrete terms how to build predictive models and to understand the advantages and limits of machine learning!
Gaël Varoquaux
© Inria / Photo É. Garault

Build predictive models with scikit-learn !

With this online course available in English, you will learn the basics of machine learning and how to use the scikit-learn Python library. This Mooc is accessible to anybody with basic Python programming skills.

The training, developed by the scikit-learn team, is mainly practical, focusing on application examples, and based on Python code executed by the participants. Everything is integrated in the Mooc and you don't have to install anything.

At the end of this course, you will be able to:

  • Grasp the fundamental concepts of machine learning
  • Build a predictive modeling pipeline with scikit-learn
  • Develop intuitions behind machine learning models from linear models to gradient-boosted decision trees
  • Evaluate the statistical performance of your models

Practical Information  


  • basic knowledge of Python programming: defining variables, writing functions, importing modules
  • previous experience with NumPy, Pandas and Matplotlib libraries is recommended but not mandatory

Schedule and registration

  • Resgitration : April 19th - July 13rd 2021
  • Courses : May 18th - July 14th 2021



Scikit-learn is our reference tool when it comes to machine learning. We are proud to be a member of the scikit-learn community and to support this leading machine learning software library. Widely used by our teams of data scientists both in France and in a dozen or so countries worldwide, this reference tool ensures a high level of reliability for the predictive models designed using it. Scikit-learn helps us to create innovative services, including the automated and accelerated processing of supporting documents in the event of a loss. It also improves internal processes, such as dispatching incoming mail or risk monitoring. Our goal is to automate 80% of all our processes between now and 2022.


Sébastien Conort


Chief Data Scientist at BNP Paribas

The creators of scikit-learn have their MOOC!

Loïc Estève

Loïc Estève

Loïc Estève’s contribution to the project involved meticulously tackling bugs. Since the consortium was set up, he has assisted the four engineers employed full-time to work on maintenance and software development. His work also involves looking for ways to make scikit-learn even more accessible and educational in order to expand its use.

Vivatech 2019

Olivier Grisel

Olivier Grisel, in charge of day to day code development, is committed to the convergence of his algorithms: “Scikit-learn’s main strength is that it offers one single programming interface that is capable of running predictive models which are highly varied from a mathematical perspective and which can be deployed in a range of scientific, commercial or industrial applications.”

Cérémonie de remise des Prix Inria 2019

Guillaume Lemaître

Since 2017, Guillaume Lemaître has contributed to the development and maintenance of scikit-learn, within the scikit-learn-Inria foundation. Currently, he is in charge of scikit-learn maintenance.

Thomas Schmitt

Thomas Schmitt

Thomas Schmitt is a Machine Learning engineer who supports the team and contributes to the Scikit-Learn library for the implementation of missing value processing algorithms and the writing of related tutorials.

Gaël Varoquaux

Gaël Varoquaux

Holder of a PhD in quantum physics from the University of Orsay, Gaël Varoquaux decided in 2008 to change direction and joined the Parietal project-team of Inria Saclay, specialized in brain modelling for neurosciences. He uses scikit-learn for his work and is involved in the animation of the developers' community. In 2018, he became project manager for the scikit-learn consortium.