Heavily based on (and shortened from) the SciPy 2015 tutorial by Kyle Kastner(@kastnerkyle) and Andreas Mueller(@t3kcit), which was, in turn, based on the SciPy 2013 tutorial by Gael Varoquaux, Olivier Grisel and Jake VanderPlas.

You can find the video recordings of the SciPy 2015 tutorial on youtube:

• Part 1
• Part 2

• Vlad Niculae (@vnfrombucharest), Cornell University

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. Try opening and running a notebook from the material to see check that it works.

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. Python2.7 and 3.4 should both work fine for this tutorial.

After getting the material, you should run python check_env.py to verify your environment.

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/vene/asda_uiuc_sklearn_tutorial.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. We 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 conferences can often be spotty, it would be a good idea to download these data sets before arriving at the conference. Run fetch_data.py to download all necessary data beforehand.

TODO