This repository contains files and other info associated with a three hour course I am on scikit-learn.
Instructor:
- Gael Varoquaux @GaelVaroquaux | http://gael-varoquaux.info
These materials can change at any time, as I work on the course
This tutorial will require recent installations of numpy, scipy, matplotlib, scikit-learn, pandas and Pillow (or PIL).
For users who do not yet have these packages installed, a relatively painless way to install all the requirements is to use a package such as Anaconda, which can be downloaded and installed for free.
Please down in advance the Olivetti and/or the LFW datasets using::
from sklearn import datasets datasets.fetch_olivetti_faces() datasets.fetch_lfw_people()
Not all the material will be covered in the course: there is not enough time available. However, you can follow the material by yourself.
The recommended way to access the materials is to execute them in the
IPython notebook. If you have the IPython notebook installed, you should
download the materials (see below), go the the notebooks directory, and
launch IPython notebook from there by typing:
cd notebooks
ipython notebook
in your terminal window. This will open a notebook panel load in your web browser.
If you don't have the IPython notebook installed, you can browse the files on Internet:
-
For the instructions and the solutions:
http://nbviewer.ipython.org/github/GaelVaroquaux/sklearn_ensae_course/tree/master/rendered_notebooks
-
For the instructions without the solutions:
http://nbviewer.ipython.org/github/GaelVaroquaux/sklearn_ensae_course/tree/master/notebooks/
I would highly recommend using git, not only for this tutorial, but for the general betterment of your life. Once git is installed, you can clone the material in this tutorial by using the git address shown above:
If you can't or don't want to install git, there is a link above to download the contents of this repository as a zip file. I may make minor changes to the repository in the days before the tutorial, however, so cloning the repository is a much better option.
The data for this tutorial is not included in the repository. We will be
using several data sets during the tutorial: most are built-in to
scikit-learn, which includes code which automatically downloads and
caches these data. Because the wireless network at the course venue can
often be spotty, it would be a good idea to download these data sets
before arriving. You can do so by using the fetch_data.py included in
the tutorial materials.
This material is adapted from the scipy 2013 tutorial:
http://github.com/jakevdp/sklearn_scipy2013
Original authors:
- Gael Varoquaux @GaelVaroquaux | http://gael-varoquaux.info
- Olivier Grisel @ogrisel | http://ogrisel.com
- Jake VanderPlas @jakevdp | http://jakevdp.github.com