Scikit-learn:external Resources, Videos and Talks

Reference: http://scikit-learn.org/stable/presentations.html

Scikit-learn's user Guide is basically finished (except for the specific Estimator section), where it extracts the additional resources provided by Scikit-learn's official website for later study.

About supervised learning and unsupervised learning involved in the estimator, when used to see it, then will be updated, but will not be like this period of time so concentrated to update.

Thank you for your support during this time.

I wish you all progress and work smoothly.

For written tutorials, see the Tutorial section of the the documentation.

New to scientific Python?

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

External Tutorials

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

• Machine learning-Neuroimaging in Python
• Machine learning-Astronomical Data analysis

Videos

• An introduction to Scikit-learn part I and part II at Scipy Gael Varoquaux, Jake Vanderplas and Olivier Grisel. Notebooks on GitHub.

• Introduction to Scikit-learn by Gael Varoquaux at ICML 2010

  A three minute video from a very early stage of the Scikit, explaining the basic idea and approach we is following.

• Introduct Ion to statistical learning with Scikit-learn by gael Varoquaux at SciPy (

  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. The material corresponding is now under the Scikit-learn documentation section  A tutorial on statistical-learning fo R Scientific Data processing .

• 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 s Olutions.

• 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 same Pydata workshop at Google

  Interactive demonstration of some Scikit-learn features. Minutes.

• Scikit-learn Tutorial by Jake Vanderplas at Pydata NYC 2012

  Presentation using the online tutorial, minutes.

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Scikit-learn:external Resources, Videos and Talks