{
 "metadata": {
  "name": "00_Preliminaries"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "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": [
      "- **1:20 - 2:50**: Part 1\n",
      "   + Getting started\n",
      "   + Loading, representing, and manipulating data\n",
      "   + Basics of Machine Learning and the scikit-learn syntax\n",
      "   + Supervised learning\n",
      "       * _regression_\n",
      "       * _classification_\n",
      "   + Unsupervised learning\n",
      "       * _clustering_\n",
      "       * _dimensionality reduction_\n",
      "\n",
      "- **2:50 - 3:10**: short break\n",
      "\n",
      "- **3:10 - 4:20**: part 2\n",
      "   + Validation and testing of models\n",
      "   + break for a short survey\n",
      "   + examples from astronomy, image classification and others"
     ]
    },
    {
     "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\n",
      "- `numpy` version 1.5 or later: http://www.numpy.org/\n",
      "- `scipy` version 0.9 or later: http://www.scipy.org/\n",
      "- `matplotlib` version 1.0 or later: http://matplotlib.org/\n",
      "- `scikit-learn` version 0.12 or later: http://scikit-learn.org\n",
      "- `ipython` version 0.13 or later, with notebook support: http://ipython.org\n",
      "\n",
      "The easiest way to get these is to use an all-in-one installer such as\n",
      "[Anaconda CE](https://store.continuum.io/) from Continuum or\n",
      "[EPD Free](http://www.enthought.com/products/epd_free.php) from Enthought.\n",
      "These are available for multiple architectures.\n",
      "\n",
      "Other options do exist:\n",
      "\n",
      "- **Linux**: If you're on Linux, you can use the linux distribution tools (by typing, for\n",
      "example `apt-get install numpy` or `yum install numpy`.\n",
      "\n",
      "- **Mac**: If you're on OSX, there are similar tools such as MacPorts or HomeBrew which\n",
      "contain pre-compiled versions of these packages.\n",
      "\n",
      "- **Windows**: Windows can be challenging: the best bet is probably to use one of the package\n",
      "installers mentioned above.\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": [
      "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__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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)\n",
      "- **astroML:** http://astroML.github.com (shameless plug: this is my project!)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Survey/Evaluation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The PyCon organizers have put together a survey for tutorial attendees.\n",
      "\n",
      "Near the end of this tutorial, please follow the link below and fill out this survey:\n",
      "\n",
      "https://www.surveymonkey.com/s/pycon2013_tutorials"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}