Lecture materials for Microsoft Practice of Machine Learning Conference, Oct 23-24 2014.
This is a one-hour introductory tutorial to the scikit-learn package.
You can follow along by installing Python and several dependencies, numpy, scipy, matplotlib, ipython, and (of course) scikit-learn. See the Setup and Installation section below.
If you do not have this Python stack installed on your system, you can follow-along with a static view of this material on nbviewer here.
To get Python up and running across multiple platforms, I recommend the Anaconda installer (or, for a lighter-weight install, use miniconda) If you're using Anaconda, you can assure that you're using the latest scikit-learn release by running:
$ conda update conda
$ conda install scikit-learn
If your system has a suitably set-up C compiler, scikit-learn can be installed using pip:
$ pip install scikit-learn
You can have a little more control by installing scikit-learn from source. The source is available using the git version management tool:
$ git clone https://github.com/scikit-learn/scikit-learn.git
$ cd scikit-learn
$ python setup.py install
Scikit-learn requires NumPy and SciPy, and examples require Matplotlib.
Note: some examples below require the scripts in the fig_code directory, which
can be found within the Github repository at http://github.com/jakevdp/PMLC-2014