.. _testimonials:
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Who is using scikit-learn?
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.. to add a testimonials, just XXX
`Spotify
`_
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Scikit-learn provides a toolbox with solid implementations of a bunch of
state-of-the-art models and makes it easy to plug them into existing
applications. We've been using it quite a lot for music recommendations at
Spotify and I think it's the most well-designed ML package I've seen so
far.
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Erik Bernhardsson, Engineering Manager Music Discovery & Machine Learning, Spotify
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`Inria `_
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:target: 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 `_ for neuroimaging, `Lear
`_ for computer vision, `Visages
`_ for medical image analysis, `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 `_
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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 `_
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Mark Ayzenshtat, VP, Augmented Intelligence
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`Télécom ParisTech `_
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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 `_
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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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`Yhat `_
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The combination of consistent APIs, thorough documentation, and top notch
implementation make scikit-learn our favorite machine learning package in
Python. scikit-learn makes doing advanced analysis in Python accessible to
anyone. At Yhat, we make it easy to integrate these models into your production
applications. Thus eliminating the unnecessary dev time encountered
productionizing analytical work.
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Greg Lamp, Co-founder Yhat
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`Rangespan `_
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.. image:: images/rangespan.png
:target: https://www.rangespan.com
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The Python scikit-learn toolkit is a core tool in the data science
group at Rangespan. Its large collection of well documented models and
algorithms allow our team of data scientists to prototype fast and
quickly iterate to find the right solution to our learning problems.
We find that scikit-learn is not only the right tool for prototyping,
but its careful and well tested implementation give us the confidence
to run scikit-learn models in production.
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Jurgen Van Gael, Data Science Director at Rangespan Ltd
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`Birchbox `_
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At Birchbox, we face a range of machine learning problems typical to
E-commerce: product recommendation, user clustering, inventory prediction,
trends detection, etc. Scikit-learn lets us experiment with many models,
especially in the exploration phase of a new project: the data can be passed
around in a consistent way; models are easy to save and reuse; updates keep us
informed of new developments from the pattern discovery research community.
Scikit-learn is an important tool for our team, built the right way in the
right language.
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Thierry Bertin-Mahieux, Birchbox, Data Scientist
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`Bestofmedia Group `_
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Scikit-learn is our #1 toolkit for all things machine learning
at Bestofmedia. We use it for a variety of tasks (e.g. spam fighting,
ad click prediction, various ranking models) thanks to the varied,
state-of-the-art algorithm implementations packaged into it.
In the lab it accelerates prototyping of complex pipelines. In
production I can say it has proven to be robust and efficient enough
to be deployed for business critical components.
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Eustache Diemert, Lead Scientist Bestofmedia Group
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`Change.org `_
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At change.org we automate the use of scikit-learn's RandomForestClassifier
in our production systems to drive email targeting that reaches millions
of users across the world each week. In the lab, scikit-learn's ease-of-use,
performance, and overall variety of algorithms implemented has proved invaluable
in giving us a single reliable source to turn to for our machine-learning needs.
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Vijay Ramesh, Software Engineer in Data/science at Change.org
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`PHIMECA Engineering `_
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At PHIMECA Engineering, we use scikit-learn estimators as surrogates for
expensive-to-evaluate numerical models (mostly but not exclusively
finite-element mechanical models) for speeding up the intensive post-processing
operations involved in our simulation-based decision making framework.
Scikit-learn's fit/predict API together with its efficient cross-validation
tools considerably eases the task of selecting the best-fit estimator. We are
also using scikit-learn for illustrating concepts in our training sessions.
Trainees are always impressed by the ease-of-use of scikit-learn despite the
apparent theoretical complexity of machine learning.
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Vincent Dubourg, PHIMECA Engineering, PhD Engineer
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