|
| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "markdown", |
| 5 | + "metadata": {}, |
| 6 | + "source": [ |
| 7 | + "<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>" |
| 8 | + ] |
| 9 | + }, |
| 10 | + { |
| 11 | + "cell_type": "markdown", |
| 12 | + "metadata": {}, |
| 13 | + "source": [ |
| 14 | + "# An Introduction to scikit-learn: Machine Learning in Python" |
| 15 | + ] |
| 16 | + }, |
| 17 | + { |
| 18 | + "cell_type": "markdown", |
| 19 | + "metadata": {}, |
| 20 | + "source": [ |
| 21 | + "## Goals of this Tutorial" |
| 22 | + ] |
| 23 | + }, |
| 24 | + { |
| 25 | + "cell_type": "markdown", |
| 26 | + "metadata": {}, |
| 27 | + "source": [ |
| 28 | + "- **Introduce the basics of Machine Learning**, and some skills useful in practice.\n", |
| 29 | + "- **Introduce the syntax of scikit-learn**, so that you can make use of the rich toolset available." |
| 30 | + ] |
| 31 | + }, |
| 32 | + { |
| 33 | + "cell_type": "markdown", |
| 34 | + "metadata": {}, |
| 35 | + "source": [ |
| 36 | + "## Schedule:" |
| 37 | + ] |
| 38 | + }, |
| 39 | + { |
| 40 | + "cell_type": "markdown", |
| 41 | + "metadata": {}, |
| 42 | + "source": [ |
| 43 | + "**Preliminaries: Setup & introduction** (15 min)\n", |
| 44 | + "* Making sure your computer is set-up\n", |
| 45 | + "\n", |
| 46 | + "**Basic Principles of Machine Learning and the Scikit-learn Interface** (45 min)\n", |
| 47 | + "* What is Machine Learning?\n", |
| 48 | + "* Machine learning data layout\n", |
| 49 | + "* Supervised Learning\n", |
| 50 | + " - Classification\n", |
| 51 | + " - Regression\n", |
| 52 | + " - Measuring performance\n", |
| 53 | + "* Unsupervised Learning\n", |
| 54 | + " - Clustering\n", |
| 55 | + " - Dimensionality Reduction\n", |
| 56 | + " - Density Estimation\n", |
| 57 | + "* Evaluation of Learning Models\n", |
| 58 | + "* Choosing the right algorithm for your dataset\n", |
| 59 | + "\n", |
| 60 | + "**Supervised learning in-depth** (1 hr)\n", |
| 61 | + "* Support Vector Machines\n", |
| 62 | + "* Decision Trees and Random Forests\n", |
| 63 | + "\n", |
| 64 | + "**Unsupervised learning in-depth** (1 hr)\n", |
| 65 | + "* Principal Component Analysis\n", |
| 66 | + "* K-means Clustering\n", |
| 67 | + "* Gaussian Mixture Models\n", |
| 68 | + "\n", |
| 69 | + "**Model Validation** (1 hr)\n", |
| 70 | + "* Validation and Cross-validation" |
| 71 | + ] |
| 72 | + }, |
| 73 | + { |
| 74 | + "cell_type": "markdown", |
| 75 | + "metadata": {}, |
| 76 | + "source": [ |
| 77 | + "## Preliminaries" |
| 78 | + ] |
| 79 | + }, |
| 80 | + { |
| 81 | + "cell_type": "markdown", |
| 82 | + "metadata": {}, |
| 83 | + "source": [ |
| 84 | + "This tutorial requires the following packages:\n", |
| 85 | + "\n", |
| 86 | + "- Python version 2.7 or 3.4+\n", |
| 87 | + "- `numpy` version 1.8 or later: http://www.numpy.org/\n", |
| 88 | + "- `scipy` version 0.15 or later: http://www.scipy.org/\n", |
| 89 | + "- `matplotlib` version 1.3 or later: http://matplotlib.org/\n", |
| 90 | + "- `scikit-learn` version 0.15 or later: http://scikit-learn.org\n", |
| 91 | + "- `ipython`/`jupyter` version 3.0 or later, with notebook support: http://ipython.org\n", |
| 92 | + "- `seaborn`: version 0.5 or later, used mainly for plot styling\n", |
| 93 | + "\n", |
| 94 | + "The easiest way to get these is to use the [conda](http://store.continuum.io/) environment manager.\n", |
| 95 | + "I suggest downloading and installing [miniconda](http://conda.pydata.org/miniconda.html).\n", |
| 96 | + "\n", |
| 97 | + "The following command will install all required packages:\n", |
| 98 | + "```\n", |
| 99 | + "$ conda install numpy scipy matplotlib scikit-learn ipython-notebook\n", |
| 100 | + "```\n", |
| 101 | + "\n", |
| 102 | + "Alternatively, you can download and install the (very large) Anaconda software distribution, found at https://store.continuum.io/." |
| 103 | + ] |
| 104 | + }, |
| 105 | + { |
| 106 | + "cell_type": "markdown", |
| 107 | + "metadata": {}, |
| 108 | + "source": [ |
| 109 | + "### Checking your installation\n", |
| 110 | + "\n", |
| 111 | + "You can run the following code to check the versions of the packages on your system:\n", |
| 112 | + "\n", |
| 113 | + "(in IPython notebook, press `shift` and `return` together to execute the contents of a cell)" |
| 114 | + ] |
| 115 | + }, |
| 116 | + { |
| 117 | + "cell_type": "code", |
| 118 | + "execution_count": null, |
| 119 | + "metadata": { |
| 120 | + "collapsed": false |
| 121 | + }, |
| 122 | + "outputs": [], |
| 123 | + "source": [ |
| 124 | + "from __future__ import print_function\n", |
| 125 | + "\n", |
| 126 | + "import IPython\n", |
| 127 | + "print('IPython:', IPython.__version__)\n", |
| 128 | + "\n", |
| 129 | + "import numpy\n", |
| 130 | + "print('numpy:', numpy.__version__)\n", |
| 131 | + "\n", |
| 132 | + "import scipy\n", |
| 133 | + "print('scipy:', scipy.__version__)\n", |
| 134 | + "\n", |
| 135 | + "import matplotlib\n", |
| 136 | + "print('matplotlib:', matplotlib.__version__)\n", |
| 137 | + "\n", |
| 138 | + "import sklearn\n", |
| 139 | + "print('scikit-learn:', sklearn.__version__)\n", |
| 140 | + "\n", |
| 141 | + "import seaborn\n", |
| 142 | + "print('seaborn', seaborn.__version__)" |
| 143 | + ] |
| 144 | + }, |
| 145 | + { |
| 146 | + "cell_type": "markdown", |
| 147 | + "metadata": {}, |
| 148 | + "source": [ |
| 149 | + "## Useful Resources" |
| 150 | + ] |
| 151 | + }, |
| 152 | + { |
| 153 | + "cell_type": "markdown", |
| 154 | + "metadata": {}, |
| 155 | + "source": [ |
| 156 | + "- **scikit-learn:** http://scikit-learn.org (see especially the narrative documentation)\n", |
| 157 | + "- **matplotlib:** http://matplotlib.org (see especially the gallery section)\n", |
| 158 | + "- **IPython:** http://ipython.org (also check out http://nbviewer.ipython.org)" |
| 159 | + ] |
| 160 | + } |
| 161 | + ], |
| 162 | + "metadata": { |
| 163 | + "kernelspec": { |
| 164 | + "display_name": "Python 3", |
| 165 | + "language": "", |
| 166 | + "name": "python3" |
| 167 | + }, |
| 168 | + "language_info": { |
| 169 | + "codemirror_mode": { |
| 170 | + "name": "ipython", |
| 171 | + "version": 3 |
| 172 | + }, |
| 173 | + "file_extension": ".py", |
| 174 | + "mimetype": "text/x-python", |
| 175 | + "name": "python", |
| 176 | + "nbconvert_exporter": "python", |
| 177 | + "pygments_lexer": "ipython3", |
| 178 | + "version": "3.5.1" |
| 179 | + } |
| 180 | + }, |
| 181 | + "nbformat": 4, |
| 182 | + "nbformat_minor": 0 |
| 183 | +} |
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