Based on the SciPy 2015 tutorial by Kyle Kastner and Andreas Mueller ).
- Sebastian Raschka @rasbt - Michigan State University, Computational Biology
- Andreas Mueller @t3kcit - NYU Center for Data Science
This repository will contain files and other info associated with our SciPy 2016 scikit-learn tutorial.
Parts 1 to 5 make up the morning session, while parts 6 to 9 will be presented in the afternoon.
This tutorial will require recent installations of NumPy, SciPy, matplotlib, scikit-learn and IPython together with the Jupyter Notebook.
The last one is important, you should be able to type:
jupyter 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 Python distribution
such as Anaconda CE, which includes
the most relevant Python packages for science, math, engineering, and
data analysis can be; Anaconda can be downloaded and installed for free
including commercial use and redistribution.
The code examples in this tutorial should be compatible to Python 2.7,
Python 3.4, and Python 3.5.
After obtaining 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/amueller/scipy_2016_sklearn.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.
To come