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‎.gitignore

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# PyBuilder
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target/
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# IPython
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.ipynb_checkpoints
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notebooks/.ipynb_checkpoints
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# Emacs
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*~

‎README.md

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# sklearn_tutorial
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Materials for my scikit-learn tutorial
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# Scikit-learn Tutorial
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*Jake VanderPlas*
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- email: <[email protected]>
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- twitter: [@jakevdp](https://twitter.com/jakevdp)
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- github: [jakevdp](http://github.com/jakevdp)
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This repository contains notebooks and other files associated with my
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[Scikit-learn](http://scikit-learn.org) tutorial.
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## Installation Notes
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This tutorial requires the following packages:
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- Python version 2.6-2.7 or 3.3+
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- `numpy` version 1.5 or later: http://www.numpy.org/
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- `scipy` version 0.10 or later: http://www.scipy.org/
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- `matplotlib` version 1.3 or later: http://matplotlib.org/
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- `scikit-learn` version 0.14 or later: http://scikit-learn.org
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- `ipython` version 2.0 or later, with notebook support: http://ipython.org
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- `seaborn` version 0.5 or later
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The easiest way to get these is to use the [conda](https://store.continuum.io/) environment manager.
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I suggest downloading and installing [miniconda](http://conda.pydata.org/miniconda.html).
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Once this is installed, the following command will install all required packages in your Python environment:
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```
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$ conda install numpy scipy matplotlib scikit-learn ipython-notebook seaborn
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```
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Alternatively, you can download and install the (very large) Anaconda software distribution, found at https://store.continuum.io/.
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## Downloading the Tutorial Materials
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I would highly recommend using git, not only for this tutorial, but for the
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general betterment of your life. Once git is installed, you can clone the
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material in this tutorial by using the git address shown above:
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git clone git://github.com/jakevdp/sklearn_tutorial.git
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If you can't or don't want to install git, there is a link above to download
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the contents of this repository as a zip file. I may make minor changes to
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the repository in the days before the tutorial, however, so cloning the
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repository is a much better option.
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## Notebook Listing
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You can [view the tutorial materials](http://nbviewer.ipython.org/github/jakevdp/sklearn_tutorial/blob/master/notebooks/Index.ipynb) using the excellent nbviewer service.
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Note, however, that you cannot modify or run the contents within nbviewer.
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To modify them, first download the tutorial repository, change to the notebooks directory, and run ``ipython notebook``.
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You should see the list in the ipython notebook launch page in your web browser.
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For more information on the IPython notebook, see http://ipython.org/notebook.html
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Note also that some of the code in these notebooks will not work outside the
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directory structure of this tutorial, so it is important to clone the full
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repository if possible.

‎notebooks/01-Preliminaries.ipynb

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{
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"metadata": {
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"name": "",
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"signature": "sha256:e15002059f80bd12a6b29c25b2179198b517b5b98da80bcfa018729797f25aea"
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},
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"nbformat": 3,
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"nbformat_minor": 0,
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"worksheets": [
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<small><i>This notebook was put together by [Jake Vanderplas](http://www.vanderplas.com). Source and license info is on [GitHub](https://github.com/jakevdp/sklearn_tutorial/).</i></small>"
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]
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},
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{
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"cell_type": "heading",
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"level": 1,
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"metadata": {},
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"source": [
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"An Introduction to scikit-learn: Machine Learning in Python"
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]
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},
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{
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"cell_type": "heading",
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"level": 2,
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"metadata": {},
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"source": [
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"Goals of this Tutorial"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"- **Introduce the basics of Machine Learning**, and some skills useful in practice.\n",
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"- **Introduce the syntax of scikit-learn**, so that you can make use of the rich toolset available."
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]
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},
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{
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"cell_type": "heading",
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"level": 2,
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"metadata": {},
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"source": [
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"Schedule:"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**10:00 - 10:15** Preliminaries: Setup & introduction\n",
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"* Making sure your computer is set-up\n",
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"\n",
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"**10:15 - 11:00** Basic Principles of Machine Learning and the Scikit-learn Interface\n",
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"* What is Machine Learning?\n",
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"* Machine learning data layout\n",
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"* Supervised Learning\n",
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" - Classification\n",
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" - Regression\n",
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" - Measuring performance\n",
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"* Unsupervised Learning\n",
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" - Clustering\n",
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" - Dimensionality Reduction\n",
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" - Density Estimation\n",
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"* Evaluation of Learning Models\n",
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"* Choosing the right algorithm for your dataset\n",
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"\n",
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"**11:00 - 12:00** Supervised learning in-depth\n",
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"* Support Vector Machines\n",
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"* Decision Trees and Random Forests\n",
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"\n",
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"*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!*"
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]
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},
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{
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"cell_type": "heading",
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"level": 2,
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"metadata": {},
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"source": [
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"Preliminaries"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This tutorial requires the following packages:\n",
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"\n",
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"- Python version 2.6-2.7 or 3.3-3.4\n",
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"- `numpy` version 1.5 or later: http://www.numpy.org/\n",
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"- `scipy` version 0.10 or later: http://www.scipy.org/\n",
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"- `matplotlib` version 1.3 or later: http://matplotlib.org/\n",
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"- `scikit-learn` version 0.14 or later: http://scikit-learn.org\n",
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"- `ipython` version 2.0 or later, with notebook support: http://ipython.org\n",
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"- `seaborn`: version 0.5 or later, used mainly for plot styling\n",
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"\n",
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"The easiest way to get these is to use the [conda](http://store.continuum.io/) environment manager.\n",
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"I suggest downloading and installing [miniconda](http://conda.pydata.org/miniconda.html).\n",
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"\n",
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"The following command will install all required packages:\n",
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"```\n",
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"$ conda install numpy scipy matplotlib scikit-learn ipython-notebook\n",
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"```\n",
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"\n",
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"Alternatively, you can download and install the (very large) Anaconda software distribution, found at https://store.continuum.io/."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Checking your installation\n",
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"\n",
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"You can run the following code to check the versions of the packages on your system:\n",
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"\n",
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"(in IPython notebook, press `shift` and `return` together to execute the contents of a cell)"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"from __future__ import print_function\n",
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"\n",
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"import IPython\n",
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"print('IPython:', IPython.__version__)\n",
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"\n",
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"import numpy\n",
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"print('numpy:', numpy.__version__)\n",
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"\n",
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"import scipy\n",
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"print('scipy:', scipy.__version__)\n",
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"\n",
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"import matplotlib\n",
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"print('matplotlib:', matplotlib.__version__)\n",
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"\n",
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"import sklearn\n",
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"print('scikit-learn:', sklearn.__version__)\n",
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"\n",
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"import seaborn\n",
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"print('seaborn', seaborn.__version__)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"IPython: 2.4.1\n",
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"numpy:"
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]
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},
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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" 1.9.2\n",
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"scipy: 0.15.1\n",
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"matplotlib: 1.4.3\n",
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"scikit-learn:"
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]
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},
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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" 0.15.2\n",
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"seaborn"
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]
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},
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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" 0.5.1\n"
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]
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}
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],
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"prompt_number": 1
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},
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{
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"cell_type": "heading",
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"level": 2,
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"metadata": {},
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"source": [
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"Useful Resources"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"- **scikit-learn:** http://scikit-learn.org (see especially the narrative documentation)\n",
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"- **matplotlib:** http://matplotlib.org (see especially the gallery section)\n",
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"- **IPython:** http://ipython.org (also check out http://nbviewer.ipython.org)"
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]
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}
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],
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"metadata": {}
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}
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]
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}

‎notebooks/02.1-Machine-Learning-Intro.ipynb

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