PyCon 2013 Scikit-learn Tutorial

Note: for updated tutorial content, please see http://github.com/jakevdp/sklearn_tutorial/

Instructor: Jake VanderPlas

• email: jakevdp@cs.washington.edu
• twitter: @jakevdp
• github: jakevdp

This repository will contain files and other info associated with my PyCon 2013 scikit-learn tutorial.

Installation Notes

This tutorial will require recent installations of numpy, scipy, matplotlib, scikit-learn, and ipython with ipython notebook. The last one is important: you should be able to type

ipython notebook

in your terminal window and see the notebook panel load in your web browser. Because Python 3 compatibility is still being ironed-out for these packages (we're getting close, I promise!) participants should plan to use Python 2.6 or 2.7 for this tutorial.

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 CE, which can be downloaded and installed for free.

Downloading the Tutorial Materials

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:

git clone git://github.com/jakevdp/sklearn_pycon2013.git

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.

Data Downloads

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, and one is culled from the Sloan Digital Sky Survey. The tutorial includes code which automatically downloads and caches these data. Because the wireless network at conferences can often be spotty, it would be a good idea to download these data sets before arriving at the conference.

To cache the required data on your computer, please download the tutorial materials as described above, and execute the script called download_data.py from the top directory. It will cache several datasets to the appropriate places, and you'll be ready to go when it comes time for the tutorial!

Notebook Listing

These notebooks in this repository can be statically viewed using the excellent nbviewer site. They will not be able to be modified within nbviewer. To modify them, first download the tutorial repository, change to the notebooks directory, and type ipython notebook. You should see the list in the ipython notebook launch page in your web browser.

• 01_introduction.ipynb
• 02_sklearn_data.ipynb
• 03_machine_learning_101.ipynb
• 04_houses_regression.ipynb
• 05_iris_classification.ipynb
• 06_iris_dimensionality.ipynb
• 07_iris_clustering.ipynb
• 08_linearly_separable.ipynb
• 09_validation_and_testing.ipynb
• 10_digits_classification.ipynb
• 11_photoz_regression.ipynb

Note that some of the code in these notebooks will not work outside the directory structure of this tutorial.