13. External Resources, Videos and Talks#
13.1. 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.
13.2. Videos#
The scikit-learn YouTube channel features a playlist of videos showcasing talks by maintainers and community members.
13.2.1. Older videos#
An introduction to scikit-learn at Scipy 2013 by Gael Varoquaux, Jake Vanderplas and Olivier Grisel.
Notebooks available on github.
Introduction to scikit-learn by Gael Varoquaux at ICML 2010
A three minute video from a very early stage of scikit-learn, explaining the basic idea and approach we are following.
Introduction to statistical learning with scikit-learn by Gael Varoquaux at SciPy 2011
An extensive tutorial, consisting of four sessions of one hour. The tutorial covers the basics of machine learning, many algorithms and how to apply them using scikit-learn.
Statistical Learning for Text Classification with scikit-learn and NLTK (and slides) by Olivier Grisel at PyCon 2011
Thirty minute introduction to text classification. Explains how to use NLTK and scikit-learn to solve real-world text classification tasks and compares against cloud-based solutions.
Introduction to Interactive Predictive Analytics in Python with scikit-learn by Olivier Grisel at PyCon 2012
3-hours long introduction to prediction tasks using scikit-learn.
scikit-learn - Machine Learning in Python by Jake Vanderplas at the 2012 PyData workshop at Google
Interactive demonstration of some scikit-learn features. 75 minutes.
scikit-learn tutorial by Jake Vanderplas at PyData NYC 2012
Presentation using the online tutorial, 45 minutes.
13.3. 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.
13.4. External Tutorials#
There are several online tutorials available which are geared toward specific subject areas: