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:

Preliminaries: Setup & introduction (15 min)

Making sure your computer is set-up

Basic Principles of Machine Learning and the Scikit-learn Interface (45 min)

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

Supervised learning in-depth (15 minutes)

Decision Trees and Random Forests

Unsupervised learning in-depth (15 minutes)

Principal Component Analysis
K-means Clustering

Model Validation (15 minutes)

Validation and Cross-validation
GridSearchCV

Other topics

Pipeline

Preliminaries

This tutorial requires the following packages:

Python version 2.7 or 3.5+
numpy version 1.8 or later: http://www.numpy.org/
scipy version 0.15 or later: http://www.scipy.org/
matplotlib version 1.3 or later: http://matplotlib.org/
scikit-learn version 0.15 or later: http://scikit-learn.org
ipython/jupyter version 3.0 or later, with notebook support: http://ipython.org

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)



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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__)

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)



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