15.1. The scikit-learn MOOC#

If you are new to scikit-learn, or looking to strengthen your understanding, we highly recommend the scikit-learn MOOC (Massive Open Online Course).

The MOOC, created and maintained by some of the scikit-learn core-contributors, is free of charge and is designed to help learners of all levels master machine learning using scikit-learn. It covers topics from the fundamental machine learning concepts to more advanced areas like predictive modeling pipelines and model evaluation.

The course materials are available on the scikit-learn MOOC website.

This course is also hosted on the FUN platform, which additionally makes the content interactive without the need to install anything, and gives access to a discussion forum.

The videos are available on the Inria Learning Lab channel in a playlist.

15.2. Videos#

• The scikit-learn YouTube channel features a playlist of videos showcasing talks by maintainers and community members.

15.3. New to Scientific Python?#

For those that are still new to the scientific Python ecosystem, we highly recommend the Python Scientific Lecture Notes. This will help you find your footing a bit and will definitely improve your scikit-learn experience. A basic understanding of NumPy arrays is recommended to make the most of scikit-learn.

15.4. External Tutorials#

There are several online tutorials available which are geared toward specific subject areas:

• Machine Learning for NeuroImaging in Python

• Machine Learning for Astronomical Data Analysis