This notebook was put together by [Jake Vanderplas](http://www.vanderplas.com) for PyData Seattle 2015. Source and license info is on [GitHub](https://github.com/jakevdp/sklearn_pydata2015/).

An Introduction to scikit-learn: Machine Learning in Python

Goals of this Tutorial

Introduce the basics of Machine Learning, and some skills useful in practice.
Introduce the syntax of scikit-learn, so that you can make use of the rich toolset available.

Schedule:

10:00 - 10:15 Preliminaries: Setup & introduction

Making sure your computer is set-up

10:15 - 11:00 Basic Principles of Machine Learning and the Scikit-learn Interface

What is Machine Learning?
Machine learning data layout
Supervised Learning
- Classification
- Regression
- Measuring performance
Unsupervised Learning
- Clustering
- Dimensionality Reduction
- Density Estimation
Evaluation of Learning Models
Choosing the right algorithm for your dataset

11:00 - 12:00 Supervised learning in-depth

Support Vector Machines
Decision Trees and Random Forests

The tutorial repository contains additional material which we will not cover here. My hope is that you will find it useful to read-through on your own if you want to go deeper!

Preliminaries

This tutorial requires the following packages:

Python version 2.6-2.7 or 3.3-3.4
numpy version 1.5 or later: http://www.numpy.org/
scipy version 0.10 or later: http://www.scipy.org/
matplotlib version 1.3 or later: http://matplotlib.org/
scikit-learn version 0.14 or later: http://scikit-learn.org
ipython version 2.0 or later, with notebook support: http://ipython.org
seaborn: version 0.5 or later, used mainly for plot styling

The easiest way to get these is to use the conda environment manager. I suggest downloading and installing miniconda.

The following command will install all required packages:

$ conda install numpy scipy matplotlib scikit-learn ipython-notebook

Alternatively, you can download and install the (very large) Anaconda software distribution, found at https://store.continuum.io/.

Checking your installation

You can run the following code to check the versions of the packages on your system:

(in IPython notebook, press shift and return together to execute the contents of a cell)



In [1]:

    
from __future__ import print_function

import IPython
print('IPython:', IPython.__version__)

import numpy
print('numpy:', numpy.__version__)

import scipy
print('scipy:', scipy.__version__)

import matplotlib
print('matplotlib:', matplotlib.__version__)

import sklearn
print('scikit-learn:', sklearn.__version__)

import seaborn
print('seaborn', seaborn.__version__)









    



IPython: 2.4.1
numpy: 1.9.2
scipy: 0.15.1
matplotlib: 1.4.3
scikit-learn: 0.15.2
seaborn 0.5.1

Useful Resources

scikit-learn: http://scikit-learn.org (see especially the narrative documentation)
matplotlib: http://matplotlib.org (see especially the gallery section)
IPython: http://ipython.org (also check out http://nbviewer.ipython.org)