05.00-Machine-Learning.ipynb 4.09 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deuxième partie, introduction au Machine Learning\n",
    "Les notebooks qui suivent sont issu d'un livre \"Python Data Science Handbook\" ( https://github.com/jakevdp/PythonDataScienceHandbook ). L'auteur est l'un des plus grands contributeurs au projet Pandas, que vous aurez l'occasion d'utiliser pendant votre projet. Son livre est une référence et il propose de nombeux notebooks sur son Github pour apprendre. Nous vous en avons sélectionné quelques uns pour introduire le Machine Learning. Ne vous inquiétez pas si vous ne comprenez pas tout, vous aurez le temps de jouer avec scikit-learn et de mieux comprendre demain avec Sylvain Rousseau.\n",
    "\n",
    "Suivez le notebook en prenant soin de comprendre les explications et le code (quand il y en a). Si vous avez besoin d'explications, n'hésitez pas à parler aux tuteurs et/ou poser vos questions sur slack ! Les tuteurs ne sont pas forcément experts et ne pourront pas répondre à toutes vos questions, mais lancer la réflexion avec eux et les autres étudiants est bénéfique !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In many ways, machine learning is the primary means by which data science manifests itself to the broader world.\n",
    "Machine learning is where these computational and algorithmic skills of data science meet the statistical thinking of data science, and the result is a collection of approaches to inference and data exploration that are not about effective theory so much as effective computation.\n",
    "\n",
    "The term \"machine learning\" is sometimes thrown around as if it is some kind of magic pill: *apply machine learning to your data, and all your problems will be solved!*\n",
    "As you might expect, the reality is rarely this simple.\n",
    "While these methods can be incredibly powerful, to be effective they must be approached with a firm grasp of the strengths and weaknesses of each method, as well as a grasp of general concepts such as bias and variance, overfitting and underfitting, and more.\n",
    "\n",
    "This chapter will dive into practical aspects of machine learning, primarily using Python's [Scikit-Learn](http://scikit-learn.org) package.\n",
    "This is not meant to be a comprehensive introduction to the field of machine learning; that is a large subject and necessitates a more technical approach than we take here. Rather, the goals of this chapter are:\n",
    "\n",
    "- To introduce the fundamental vocabulary and concepts of machine learning.\n",
    "- To introduce the Scikit-Learn API and show some examples of its use.\n",
    "- To take a deeper dive into the details of several of the most important machine learning approaches, and develop an intuition into how they work and when and where they are applicable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Allez au prochain notebook** \"05.01-What-Is-Machine-Learning\""
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "navigate_num": "#000000",
    "navigate_text": "#333333",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700",
    "sidebar_border": "#EEEEEE",
    "wrapper_background": "#FFFFFF"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "48px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false,
   "widenNotebook": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}