01_introduction.ipynb

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       "<style>\n",
       "    \n",
       "    @import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
       "    \n",
       "    @import url('http://fonts.googleapis.com/css?family=Kameron');\n",
       "    @import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
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    "# IGNORE THIS CELL WHICH CUSTOMIZES LAYOUT AND STYLING OF THE NOTEBOOK !\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore', category=FutureWarning)\n",
    "warnings.filterwarnings = lambda *a, **kw: None\n",
    "from IPython.core.display import HTML; HTML(open(\"custom.html\", \"r\").read())"
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  {
   "cell_type": "markdown",
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   "source": [
    "# Chapter 1: General Introduction to machine learning (ML)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ML = \"learning models from data\"\n",
    "\n",
    "\n",
    "### About models\n",
    "\n",
    "A \"model\" allows us to explain observations and to answer questions. For example:\n",
    "\n",
    "   1. Where will my car at given velocity stop if I apply break now?\n",
    "   2. Where on the night sky will I see the moon tonight?\n",
    "   3. Is the email I received spam?\n",
    "   4. What product should I recommend my customer `X` ?\n",
    "   \n",
    "- The first two questions can be answered based on existing physical models (formulas). \n",
    "\n",
    "- For the  questions 3 and 4 it is difficult to develop explicitly formulated models. \n",
    "\n",
    "### What is needed to apply ML ?\n",
    "\n",
    "\n",
    "- We have no explicit formula for such a task.\n",
    "\n",
    "\n",
    "- We have a vague understanding of the problem domain, e.g. we know that some words are specific to spam emails and others are specific to my personal and work-related emails.\n",
    "\n",
    "\n",
    "- We have enough example data, as my mailbox is full of both spam and non-spam emails.\n",
    "\n",
    "\n",
    "We could handcraft a personal spam classifier by hard coding rules, like \"mail contains 'no prescription' and comes from russia or china\" plus some statistics which would be very tedious\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<i class=\"fa fa-info-circle\"></i>\n",
    "    Systems with such hard coded rules are called <strong>expert systems</strong>\n",
    "</div>\n",
    "\n",
    "**In such cases machine learning offers approaches to automatically build predictive models based on example data.**\n",
    "\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<i class=\"fa fa-info-circle\"></i>\n",
    "The closely-related concept of <strong>data mining</strong> usually means use of predictive machine learning models to explicitly discover previously unknown knowledge from a specific data set, such as, for instance, association rules between customer and article types in the Problem 4 above.\n",
    "</div>\n",
    "\n",
    "\n",
    "\n",
    "## ML: what is \"learning\" ?\n",
    "\n",
    "To create a predictive model, we must first **train** such a model on given data. \n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<i class=\"fa fa-info-circle\"></i>\n",
    "Alternative names for \"to train\" a model are \"to <strong>fit</strong>\" or \"to <strong>learn</strong>\" a model.\n",
    "</div>\n",
    "\n",
    "\n",
    "All ML algorithms have in common that they rely on internal data structures and/or parameters. Learning then builds up such data structures or adjusts parameters based on the given data. After that such models can be used to explain observations or to answer questions.\n",
    "\n",
    "The important difference between explicit models and models learned from data:\n",