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Who is using scikit-learn?
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`Inria <http://www.inria.fr>`_
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.. title Scikit-learn for efficient and easier machine learning research
.. Author: Gaël Varoquaux


At INRIA, we use scikit-learn to support leading-edge basic research in many
teams: `Parietal <https://team.inria.fr/parietal/>`_ for neuroimaging, `Lear
<http://lear.inrialpes.fr/>`_ for computer vision, `Visages
<https://www.irisa.fr/visages/index>`_ for medical image analysis, `Privatics
<https://team.inria.fr/privatics>`_ for security. The project is a fantastic
tool to address difficult applications of machine learing in an academic
environment as it is performant and versatile, but all easy-to-use and well
documented, which makes it well suited to grad students.


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Gaël Varoquaux, research at Parietal

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`Evernote <http://evernote.com>`_
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Building a classifier is typically an iterative process of exploring
the data, selecting the features (the attributes of the data believed
to be predictive in some way), training the models, and finally
evaluating them. For many of these tasks, we relied on the excellent
scikit-learn package for Python.

`Read more <http://blog.evernote.com/tech/2013/01/22/stay-classified/>`_

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Mark Ayzenshtat, VP, Augmented Intelligence

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`Télécom ParisTech <http://www.telecom-paristech.fr>`_
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At Telecom ParisTech, scikit-learn is used for hands-on sessions and home
assignments in introductory and advanced machine learning courses. The classes
are for undergrads and masters students. The great benefit of scikit-learn is
its fast learning curve that allows students to quickly start working on
interesting and motivating problems.

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Alexandre Gramfort, Assistant Professor

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`AWeber <http://aweber.com/>`_
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The scikit-learn toolkit is indispensable for the Data Analysis and Management
team at AWeber.  It allows us to do AWesome stuff we would not otherwise have
the time or resources to accomplish. The documentation is excellent, allowing
new engineers to quickly evaluate and apply many different algorithms to our
data. The text feature extraction utilities are useful when working with the
large volume of email content we have at AWeber. The RandomizedPCA
implementation, along with Pipelining and FeatureUnions, allows us to develop
complex machine learning algorithms efficiently and reliably.

Anyone interested in learning more about how AWeber deploys scikit-learn in a
production environment should check out talks from PyData Boston by AWeber's
Michael Becker available at https://github.com/mdbecker/pydata_2013

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Michael Becker, Software Engineer, Data Analysis and Management Ninjas

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