Introduction to scikit-learn
Master M2: Mathématiques, Apprentissage et Sciences Humaines (MASH) 2016-2017 course
- email: firstname.lastname@example.org
This repository contains notebooks and other files associated with the MASH course introduction to Scikit-learn.
You can view the teaching materials using the excellent nbviewer service.
- 00: Introduction to Python
- 01: Introduction to Pandas and scikit-learn
- 02: Supervised learning I
- etc. (see github page)
Note, however, that you cannot modify or run the contents within nbviewer.
To modify them, first download the tutorial repository, change to the notebooks directory, and run
You should see the list in the ipython notebook launch page in your web browser.
For more information on the IPython notebook, see http://ipython.org/notebook.html
Note also that some of the code in these notebooks will not work outside the directory structure of this tutorial, so it is important to clone the full repository if possible.
This tutorial requires the following packages:
- Python version 2.6-2.7 or 3.3+
numpyversion 1.5 or later: http://www.numpy.org/
scipyversion 0.10 or later: http://www.scipy.org/
matplotlibversion 1.3 or later: http://matplotlib.org/
scikit-learnversion 0.14 or later: http://scikit-learn.org
ipythonversion 2.0 or later, with notebook support: http://ipython.org
- Keras (for the last class)
Once this is installed, the following command will install all required packages in your Python environment:
$ conda install numpy scipy matplotlib scikit-learn jupyter seaborn plotly
Alternatively, you can download and install the (very large) Anaconda software distribution, found at https://store.continuum.io/.
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 https://github.com/fabianp/mash_sklearn_intro.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.
Material from last year's course
See also the excellent scikit-learn tutorial by Jake Vanderplas.