79 lines
2.2 KiB
ReStructuredText
79 lines
2.2 KiB
ReStructuredText
.. _tutorial_menu:
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.. include:: ../includes/big_toc_css.rst
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Tutorials: From the bottom up with scikit-learn
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=======================================================================
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.. topic:: New to Scientific Python?
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For those that are still new to the scientific Python ecosystem,
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we highly recommend the `Python Scientific Lecture
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Notes <http://scipy-lectures.github.io/>`_. This will help you
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find your footing a bit and will definitely improve your
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scikit-learn experience.
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.. topic:: Quick start
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In this section, we introduce the `machine learning
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<http://en.wikipedia.org/wiki/Machine_learning>`_
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vocabulary that we use through-out `scikit-learn` and give a
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simple learning example.
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.. toctree::
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:maxdepth: 2
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basic/tutorial.rst
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.. topic:: Statistical-learning Tutorial
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This tutorial covers some of the models and tools available
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to do data-processing with Scikit Learn and how to learn from
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your data.
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.. toctree::
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:maxdepth: 2
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statistical_inference/index.rst
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.. topic:: **Machine Learning Cheat Sheet (for scikit-learn)**
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This flowchart is useful for newcomers regarding how to go about
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solving problems using scikit-learn. It provides a rough guide on
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how to approach problems and which estimators to try out on your
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data. Click the image below to begin..
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.. toctree::
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:maxdepth: 2
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machine_learning_map/index.rst
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.. topic:: **External Tutorials**
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There are several online tutorials available which are geared toward
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specific subject areas:
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- `Machine Learning for NeuroImaging in Python <http://nisl.github.com/>`_
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- `Machine Learning for Astronomical Data Analysis <http://astroml.github.com/sklearn_tutorial/>`_
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.. topic:: **Videos**
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Videos with tutorials can also be found in the :ref:`videos` section.
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.. note:: **Doctest Mode**
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The code-examples in the above tutorials are written in a
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*python-console* format. If you wish to easily execute these examples
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in **iPython**, use::
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%doctest_mode
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in the iPython-console. You can then simply copy and paste the examples
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directly into iPython without having to worry about removing the **>>>**
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manually.
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