doc/presentations.rst
.. _external_resources:
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 <https://inria.github.io/scikit-learn-mooc/>_.
This course is also hosted on the FUN platform <https://www.fun-mooc.fr/en/courses/machine-learning-python-scikit-learn/>_,
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 <https://www.youtube.com/@inrialearninglab>_
in a
playlist <https://www.youtube.com/playlist?list=PL2okA_2qDJ-m44KooOI7x8tu85wr4ez4f>__.
.. _videos:
scikit-learn YouTube channel <https://www.youtube.com/@scikit-learn>_
features a
playlist <https://www.youtube.com/@scikit-learn/playlists>__
of videos
showcasing talks by maintainers
and community members.For those that are still new to the scientific Python ecosystem, we highly
recommend the Python Scientific Lecture Notes <https://scipy-lectures.org>_. 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.
There are several online tutorials available which are geared toward specific subject areas:
Machine Learning for NeuroImaging in Python <https://nilearn.github.io/>_Machine Learning for Astronomical Data Analysis <https://github.com/astroML/sklearn_tutorial>_