initial commit of tutorial materials

.gitignore

@@ -55,3 +55,10 @@ docs/_build/
 
 # PyBuilder
 target/
+
+# IPython
+.ipynb_checkpoints
+notebooks/.ipynb_checkpoints
+
+# Emacs
+*~

README.md

@@ -1,2 +1,56 @@
-# sklearn_tutorial
-Materials for my scikit-learn tutorial
+# Scikit-learn Tutorial
+
+*Jake VanderPlas*
+
+- email: <[email protected]>
+- twitter: [@jakevdp](https://twitter.com/jakevdp)
+- github: [jakevdp](http://github.com/jakevdp)
+
+This repository contains notebooks and other files associated with my
+[Scikit-learn](http://scikit-learn.org) tutorial.
+
+## Installation Notes
+This tutorial requires the following packages:
+
+- Python version 2.6-2.7 or 3.3+
+- `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
+
+The easiest way to get these is to use the [conda](https://store.continuum.io/) environment manager.
+I suggest downloading and installing [miniconda](http://conda.pydata.org/miniconda.html).
+
+Once this is installed, the following command will install all required packages in your Python environment:
+```
+$ conda install numpy scipy matplotlib scikit-learn ipython-notebook seaborn
+```
+
+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 git://github.com/jakevdp/sklearn_tutorial.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.
+
+
+## Notebook Listing
+You can [view the tutorial materials](http://nbviewer.ipython.org/github/jakevdp/sklearn_tutorial/blob/master/notebooks/Index.ipynb) using the excellent nbviewer service.
+
+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 ``ipython notebook``.
+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.

notebooks/01-Preliminaries.ipynb

@@ -0,0 +1,206 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:e15002059f80bd12a6b29c25b2179198b517b5b98da80bcfa018729797f25aea"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<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>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "An Introduction to scikit-learn: Machine Learning in Python"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Goals of this Tutorial"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "- **Introduce the basics of Machine Learning**, and some skills useful in practice.\n",
+      "- **Introduce the syntax of scikit-learn**, so that you can make use of the rich toolset available."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Schedule:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "**10:00 - 10:15** Preliminaries: Setup & introduction\n",
+      "* Making sure your computer is set-up\n",
+      "\n",
+      "**10:15 - 11:00** Basic Principles of Machine Learning and the Scikit-learn Interface\n",
+      "* What is Machine Learning?\n",
+      "* Machine learning data layout\n",
+      "* Supervised Learning\n",
+      "    - Classification\n",
+      "    - Regression\n",
+      "    - Measuring performance\n",
+      "* Unsupervised Learning\n",
+      "    - Clustering\n",
+      "    - Dimensionality Reduction\n",
+      "    - Density Estimation\n",
+      "* Evaluation of Learning Models\n",
+      "* Choosing the right algorithm for your dataset\n",
+      "\n",
+      "**11:00 - 12:00** Supervised learning in-depth\n",
+      "* Support Vector Machines\n",
+      "* Decision Trees and Random Forests\n",
+      "\n",
+      "*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!*"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Preliminaries"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This tutorial requires the following packages:\n",
+      "\n",
+      "- Python version 2.6-2.7 or 3.3-3.4\n",
+      "- `numpy` version 1.5 or later: http://www.numpy.org/\n",
+      "- `scipy` version 0.10 or later: http://www.scipy.org/\n",
+      "- `matplotlib` version 1.3 or later: http://matplotlib.org/\n",
+      "- `scikit-learn` version 0.14 or later: http://scikit-learn.org\n",
+      "- `ipython` version 2.0 or later, with notebook support: http://ipython.org\n",
+      "- `seaborn`: version 0.5 or later, used mainly for plot styling\n",
+      "\n",
+      "The easiest way to get these is to use the [conda](http://store.continuum.io/) environment manager.\n",
+      "I suggest downloading and installing [miniconda](http://conda.pydata.org/miniconda.html).\n",
+      "\n",
+      "The following command will install all required packages:\n",
+      "```\n",
+      "$ conda install numpy scipy matplotlib scikit-learn ipython-notebook\n",
+      "```\n",
+      "\n",
+      "Alternatively, you can download and install the (very large) Anaconda software distribution, found at https://store.continuum.io/."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Checking your installation\n",
+      "\n",
+      "You can run the following code to check the versions of the packages on your system:\n",
+      "\n",
+      "(in IPython notebook, press `shift` and `return` together to execute the contents of a cell)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import print_function\n",
+      "\n",
+      "import IPython\n",
+      "print('IPython:', IPython.__version__)\n",
+      "\n",
+      "import numpy\n",
+      "print('numpy:', numpy.__version__)\n",
+      "\n",
+      "import scipy\n",
+      "print('scipy:', scipy.__version__)\n",
+      "\n",
+      "import matplotlib\n",
+      "print('matplotlib:', matplotlib.__version__)\n",
+      "\n",
+      "import sklearn\n",
+      "print('scikit-learn:', sklearn.__version__)\n",
+      "\n",
+      "import seaborn\n",
+      "print('seaborn', seaborn.__version__)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "IPython: 2.4.1\n",
+        "numpy:"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " 1.9.2\n",
+        "scipy: 0.15.1\n",
+        "matplotlib: 1.4.3\n",
+        "scikit-learn:"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " 0.15.2\n",
+        "seaborn"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " 0.5.1\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Useful Resources"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "- **scikit-learn:** http://scikit-learn.org (see especially the narrative documentation)\n",
+      "- **matplotlib:** http://matplotlib.org (see especially the gallery section)\n",
+      "- **IPython:** http://ipython.org (also check out http://nbviewer.ipython.org)"
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}