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:
First your need to make sure you have Python on your machine. In order to check it you can just type:
If Python is installed, you should be able to see somethng like this:
Otherwise you will see something else. If you don't have python try to install:
On OS X install it via brew (If you don't have brew, you can inatll it here ):
brew install python
On Windows use the installer from the official website.
On Linux use the package manage of your distribution (e.g.
Both Python2.7 and 3.4 should both work fine for this tutorial.
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:
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.
If you don't have
you can install it through
pip install ipython
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.
After getting the material, you should run
python check_env.py to verify
If you are missing any package, use
pip to instal them. For example to instal'
numpy we can do:
pip install numpy
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/vene/adsa_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
fetch_data.py to download all necessary data beforehand.