01_introduction.ipynb

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   "source": [
    "# Chapter 1: General Introduction to machine learning (ML)"
   ]
  },
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   "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. Which article \"X\" should I recommend to a customer \"Y\"?\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",
    "Problems 3 and 4 have the following in common:\n",
    "\n",
    "- No exact model known or implementable because we have a vague understanding of the problem domain.\n",
    "- But enough data with sufficient and implicit information is available.\n",
    "\n",
    "\n",
    "\n",
    "E.g. for the spam email example:\n",
    "\n",
    "- We have no explicit formula for such a task (and devising one would boil down to lots of trial with different statistics or scores and possibly weighting of them).\n",
    "- We have a vague understanding of the problem domain because we know that some words are specific to spam emails and others are specific to my personal and work-related emails.\n",
    "- My mailbox is full with examples of both spam and non-spam emails.\n",
    "\n",
    "**In such cases machine learning offers approaches to build models based on example data.**\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",
    "\n",
    "- Explicit models usually offer exact answers to questions\n",
    "- Models we learn from data usually come with inherent uncertainty."
   ]
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   "source": [
    "\n",
    "## Some history\n",
    "\n",
    "Some parts of ML are older than you might think. This is a rough time line with a few selected achievements from this field:\n",
    "\n",
    "    1805: Least squares regression\n",
    "    1812: Bayes' rule\n",
    "    1913: Markov Chains\n",
    "\n",
    "    1951: First neural network\n",
    "    1957-65: \"k-means\" clustering algorithm\n",
    "    1959: Term \"machine learning\" is coined by Arthur Samuel, an AI pioneer\n",
    "    1969: Book \"Perceptrons\": Limitations of Neural Networks\n",
    "    1984: Book \"Classification And Regression Trees\"\n",
    "    1974-86: Neural networks learning breakthrough: backpropagation method\n",
    "    1995: Randomized Forests and Support Vector Machines methods\n",
    "    1998: Public appearance: first ML implementations of spam filtering methods; naive Bayes Classifier method\n",
    "    2006-12: Neural networks learning breakthrough: deep learning\n",
    "    \n",
    "So the field is not as new as one might think, but due to \n",
    "\n",
    "- more available data\n",
    "- more processing power \n",
    "- development of better algorithms \n",
    "\n",
    "more applications of machine learning appeared during the last 15 years."
   ]
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   "source": [
    "## Machine learning with Python\n",
    "\n",
    "Currently (2018) `Python` is the  dominant programming language for ML. Especially the advent of deep-learning pushed this forward. First versions of frameworks such as `TensorFlow` or `PyTorch` got early `Python` releases.\n",
    "\n",
    "The prevalent packages in the Python eco-system used for ML include:\n",
    "\n",
    "- `pandas` for handling tabular data\n",
    "- `matplotlib` and `seaborn` for plotting\n",
    "- `scikit-learn` for classical (non-deep-learning) ML\n",
    "- `TensorFlow`, `PyTorch` and `Keras` for deep-learning.\n",
    "\n",
    "`scikit-learn` is very comprehensive and the online-documentation itself provides a good introducion into ML."
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## ML lingo: What are \"features\" ?\n",
    "\n",
    "A typical and very common situation is that our data is presented as a table, as in the following example:"
   ]
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     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol_content</th>\n",
       "      <th>bitterness</th>\n",
       "      <th>darkness</th>\n",
       "      <th>fruitiness</th>\n",
       "      <th>is_yummy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.739295</td>\n",
       "      <td>0.422503</td>\n",
       "      <td>0.989463</td>\n",
       "      <td>0.215791</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.207849</td>\n",
       "      <td>0.841668</td>\n",
       "      <td>0.928626</td>\n",
       "      <td>0.380420</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.709494</td>\n",
       "      <td>0.322037</td>\n",
       "      <td>5.374682</td>\n",
       "      <td>0.145231</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.684743</td>\n",
       "      <td>0.434315</td>\n",
       "      <td>4.072805</td>\n",
       "      <td>0.191321</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.148710</td>\n",
       "      <td>0.570586</td>\n",
       "      <td>1.461568</td>\n",
       "      <td>0.260218</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol_content  bitterness  darkness  fruitiness  is_yummy\n",
       "0         3.739295    0.422503  0.989463    0.215791         0\n",
       "1         4.207849    0.841668  0.928626    0.380420         0\n",
       "2         4.709494    0.322037  5.374682    0.145231         1\n",
       "3         4.684743    0.434315  4.072805    0.191321         1\n",
       "4         4.148710    0.570586  1.461568    0.260218         0"
      ]
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   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "features = pd.read_csv(\"beers.csv\")\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<i class=\"fa fa-warning\"></i>&nbsp;<strong>Definitions</strong>\n",
    "<ul>\n",
    "    <li>every row of such a matrix is called a <strong>sample</strong> or <strong>feature vector</strong>;</li>\n",
    "    <li>the cells in a row are <strong>feature values</strong>;</li>\n",
    "    <li>every column name is called a <strong>feature name</strong> or <strong>attribute</strong>.</li>\n",
    "</ul>\n",
    "\n",
    "Features are also commonly called <strong>variables</strong>.\n",
    "</div>"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This table shown holds five samples.\n",
    "\n",
    "The feature names are `alcohol_content`, `bitterness`, `darkness`, `fruitiness` and `is_yummy`.\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<i class=\"fa fa-warning\"></i>&nbsp;<strong>More definitions</strong>\n",
    "<ul>\n",
    "    <li>The first four features have continuous numerical values within some ranges - these are called <strong>numerical features</strong>,</li>\n",
    "    <li>the <code>is_yummy</code> feature has only a finite set of values (\"categories\"): <code>0</code> (\"no\") and <code>1</code> (\"yes\") - this is called a <strong>categorical feature</strong>.</li>\n",
    "</ul>\n"
   ]
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   "source": [
    "A straight-forward application of machine-learning on the previous beer dataset is: **\"can we predict `is_yummy` from the other features\"** ?\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<i class=\"fa fa-warning\"></i>&nbsp;<strong>Even more definitions</strong>\n",
    "\n",
    "In context of the question above we call:\n",
    "<ul>\n",
    "    <li>the <code>alcohol_content</code>, <code>bitterness</code>, <code>darkness</code>, <code>fruitiness</code> features our <strong>input features</strong>, and</li>\n",
    "    <li>the <code>is_yummy</code> feature our <strong>target/output feature</strong> or a <strong>label</strong> of our data samples.\n",
    "        <ul>\n",
    "            <li>Values of categorical labels, such as <code>0</code> (\"no\") and <code>1</code> (\"yes\") here, are often called <strong>classes</strong>.</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "</ul>"
   ]
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   "metadata": {},
   "source": [
    "Most of the machine learning algorithms require that every sample is represented as a vector containing numbers. Let's look now at two examples of how one can create feature vectors from data which is not naturally given as vectors:\n",
    "\n",
    "1. Feature vectors from images\n",
    "2. Feature vectors from text.\n",
    "\n",
    "### 1st Example: How to represent images as feature vectors ?\n",
    "\n",
    "In order to simplify our explanations we only consider grayscale images in this section. \n",
    "Computers represent images as matrices. Every cell in the matrix represents one pixel, and the numerical value in the matrix cell its gray value.\n",
    "\n",
    "So how can we represent images as vectors?\n",
    "\n",
    "To demonstrate this we will now load a sample dataset that is included in `scikit-learn`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['DESCR', 'data', 'images', 'target', 'target_names']\n"
     ]
    }
   ],
   "source": [
    "dd = load_digits()\n",
    "print(dir(dd))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the first ten digits from this data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 10\n",
    "\n",
    "plt.figure(figsize=(2 * N, 5))\n",
    "\n",
    "for i, image in enumerate(dd.images[:N]):\n",
    "    plt.subplot(1, N, i + 1).set_title(dd.target[i])\n",
    "    plt.imshow(image, cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is a set of 8 x 8 matrices with values 0 to 15 (black to white). The range 0 to 15 is fixed for this specific data set. Other formats allow e.g. values 0..255 or floating point values in the range 0 to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "images.ndim: 3\n",
      "images[0].shape: (8, 8)\n",
      "images[0]:\n",
      " [[ 0.  0.  5. 13.  9.  1.  0.  0.]\n",
      " [ 0.  0. 13. 15. 10. 15.  5.  0.]\n",
      " [ 0.  3. 15.  2.  0. 11.  8.  0.]\n",
      " [ 0.  4. 12.  0.  0.  8.  8.  0.]\n",
      " [ 0.  5.  8.  0.  0.  9.  8.  0.]\n",
      " [ 0.  4. 11.  0.  1. 12.  7.  0.]\n",
      " [ 0.  2. 14.  5. 10. 12.  0.  0.]\n",
      " [ 0.  0.  6. 13. 10.  0.  0.  0.]]\n",
      "images.shape: (1797, 8, 8)\n",
      "images.size: 115008\n",
      "images.dtype: float64\n",
      "images.itemsize: 8\n",
      "target.size: 1797\n",
      "target_names: [0 1 2 3 4 5 6 7 8 9]\n",
      "DESCR:\n",
      " Optical Recognition of Handwritten Digits Data Set\n",
      "===================================================\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\n",
      "    :Number of Instances: 5620\n",
      "    :Number of Attributes: 64\n",
      "    :Attribute Information: 8x8 image of integer pixels in the range 0..16.\n",
      "    :Missing Attribute Values: None\n",
      "    :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n",
      "    :Date: July; 1998\n",
      "\n",
      "This is a copy of the test set of the UCI ML hand-written digits datasets\n",
      "http://archive.ics.uci.edu/ml/datas \n",
      "[...]\n"
     ]
    }
   ],
   "source": [
    "print(\"images.ndim:\", dd.images.ndim) # number of dimensions of the array\n",
    "print(\"images[0].shape:\", dd.images[0].shape) # dimensions of a first sample array\n",
    "print(\"images[0]:\\n\", dd.images[0]) # first sample array\n",
    "print(\"images.shape:\", dd.images.shape) # dimensions of the array of all samples\n",
    "print(\"images.size:\", dd.images.size) # total number of elements of the array\n",
    "print(\"images.dtype:\", dd.images.dtype) # type of the elements in the array\n",
    "print(\"images.itemsize:\", dd.images.itemsize) # size in bytes of each element of the array\n",
    "print(\"target.size:\", dd.target.size) # size of the target feature vector (labels of samples)\n",
    "print(\"target_names:\", dd.target_names) # classes vector\n",
    "print(\"DESCR:\\n\", dd.DESCR[:500], \"\\n[...]\") # description of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To transform such an image to a feature vector we just have to flatten the matrix by concatenating the rows to one single vector of size 64:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image_vector.shape: (64,)\n",
      "image_vector: [ 0.  0.  5. 13.  9.  1.  0.  0.  0.  0. 13. 15. 10. 15.  5.  0.  0.  3.\n",
      " 15.  2.  0. 11.  8.  0.  0.  4. 12.  0.  0.  8.  8.  0.  0.  5.  8.  0.\n",
      "  0.  9.  8.  0.  0.  4. 11.  0.  1. 12.  7.  0.  0.  2. 14.  5. 10. 12.\n",
      "  0.  0.  0.  0.  6. 13. 10.  0.  0.  0.]\n"
     ]
    }
   ],
   "source": [
    "image_vector = dd.images[0].flatten()\n",
    "print(\"image_vector.shape:\", image_vector.shape)\n",
    "print(\"image_vector:\", image_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1797, 8, 8)\n",
      "(1797, 64)\n",
      "[ 0.  0.  5. 13.  9.  1.  0.  0.  0.  0. 13. 15. 10. 15.  5.  0.  0.  3.\n",
      " 15.  2.  0. 11.  8.  0.  0.  4. 12.  0.  0.  8.  8.  0.  0.  5.  8.  0.\n",
      "  0.  9.  8.  0.  0.  4. 11.  0.  1. 12.  7.  0.  0.  2. 14.  5. 10. 12.\n",
      "  0.  0.  0.  0.  6. 13. 10.  0.  0.  0.]\n"
     ]
    }
   ],
   "source": [
    "print(dd.images.shape)\n",
    "\n",
    "# reashape to 1797, 64:\n",
    "images_flat = dd.images.reshape(-1, 64)\n",
    "print(images_flat.shape)\n",
    "print(images_flat[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2nd Example: How to present textual data as feature vectors?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we start a machine learning project for texts, we first have to choose a dictionary - set of words for this project. The final representation of a text as a feature vector depends on this dictionary.\n",
    "\n",
    "Such a dictionary can be very large, but for the sake of simplicity we use a very small enumerated dictionary to explain the overall procedure:\n",
    "\n",
    "\n",
    "| Word     | Index |\n",
    "|----------|-------|\n",
    "| like     | 0     |\n",
    "| dislike  | 1     |\n",
    "| american | 2     |\n",
    "| italian  | 3     |\n",
    "| beer     | 4     |\n",
    "| pizza    | 5     |\n",
    "\n",
    "To \"vectorize\" a given text we count the words in the text which also exist in the vocabulary and put the counts at the given `Index`.\n",
    "\n",
    "E.g. `\"I dislike american pizza, but american beer is nice\"`:\n",
    "\n",
    "| Word     | Index | Count |\n",
    "|----------|-------|-------|\n",
    "| like     | 0     | 0     |\n",
    "| dislike  | 1     | 1     |\n",
    "| american | 2     | 2     |\n",
    "| italian  | 3     | 0     |\n",
    "| beer     | 4     | 1     |\n",
    "| pizza    | 5     | 1     |\n",
    "\n",
    "The respective feature vector is the `Count` column, which is:\n",
    "\n",
    "`[0, 1, 2, 0, 1, 1]`\n",
    "\n",
    "In real case scenarios the dictionary is much bigger, which often results in vectors with only few non-zero entries (so called **sparse vectors**)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below you find is a short code example to demonstrate how text feature vectors can be created with `scikit-learn`.\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<i class=\"fa fa-info-circle\"></i>\n",
    "Such vectorization is usually not done manually. Actually there are improved but more complicated procedures which compute multiplicative weights for the vector entries to emphasize informative words such as, e.g., <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">\"term frequency-inverse document frequency\" vectorizer</a>.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from itertools import count\n",
    "\n",
    "vocabulary = {\n",
    "    \"like\": 0,\n",
    "    \"dislike\": 1,\n",
    "    \"american\": 2,\n",
    "    \"italian\": 3,\n",
    "    \"beer\": 4,\n",
    "    \"pizza\": 5,\n",
    "}\n",
    "\n",
    "vectorizer = CountVectorizer(vocabulary=vocabulary)\n",
    "\n",
    "# this how one can create a count vector for a given piece of text:\n",
    "vector = vectorizer.fit_transform([\n",
    "    \"I dislike american pizza. But american beer is nice\"\n",
    "]).toarray().flatten()\n",
    "print(vector)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ML lingo: What are the different types of datasets?\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<i class=\"fa fa-warning\"></i>&nbsp;<strong>Definitions</strong>\n",
    "\n",
    "Subset of data used for:\n",
    "<ul>\n",
    "    <li>learning (training) a model is called a <strong>training set</strong>;</li>\n",
    "    <li>improving ML method performance by adjusting its parameters is called <strong>validation set</strong>;</li>\n",
    "    <li>assesing final performance is called <strong>test set</strong>.</li>\n",
    "</ul>\n",
    "</div>\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td><img src=\"./data_split.png\" width=300px></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"font-size:75%\"><center>Img source: https://dziganto.github.io</center></td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "\n",
    "You will learn more on how to select wisely subsets of your data and about related issues later in the course. For now just remember that:\n",
    "1. the training and validation datasets must be disjunct during each iteration of the method improvement, and\n",
    "1. the test dataset must be independent from the model (hence, from the other datasets), i.e. it is indeed used only for the final assesment of the method's performance (think: locked in the safe until you're done with model tweaking).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Taxonomy of machine learning\n",
    "\n",
    "Most applications of ML belong to two categories: **supervised** and **unsupervised** learning.\n",
    "\n",
    "### Supervised learning \n",
    "\n",
    "In supervised learning the data comes with an additional target/label value that we want to predict. Such a problem can be either \n",
    "\n",
    "- **classification**: we want to predict a categorical value.\n",
    "    \n",
    "- **regression**: we want to predict numbers in a given range.\n",
    "    \n",
    "  \n",
    "\n",
    "Examples of supervised learning:\n",
    "\n",
    "- Classification: predict the class `is_yummy`  based on the attributes `alcohol_content`,\t`bitterness`, \t`darkness` and `fruitiness` (a standard two class problem).\n",
    "\n",
    "- Classification: predict the digit-shown based on a 8 x 8 pixel image (a multi-class problem).\n",
    "\n",
    "- Regression: predict temperature based on how long sun was shining in the last 10 minutes.\n",
    "\n",
    "\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "    <td><img src=\"./classification-svc-2d-poly.png\" width=400px></td>\n",
    "    <td><img src=\"./regression-lin-1d.png\" width=400px></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><center>Classification</center></td>\n",
    "        <td><center>Linear regression</center></td>\n",
    "    </tr>\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unsupervised learning \n",
    "\n",
    "In unsupervised learning the training data consists of samples without any corresponding target/label values and the aim is to find structure in data. Some common applications are:\n",
    "\n",
    "- Clustering: find groups in data.\n",
    "- Density estimation, novelty detection: find a probability distribution in your data.\n",
    "- Dimension reduction (e.g. PCA): find latent structures in your data.\n",
    "\n",
    "Examples of unsupervised learning:\n",
    "\n",
    "- Can we split up our beer data set into sub-groups of similar beers?\n",
    "- Can we reduce our data set because groups of features are somehow correlated?\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "    <td><img src=\"./cluster-image.png/\" width=400px></td>\n",
    "    <td><img src=\"./nonlin-pca.png/\" width=400px></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><center>Clustering</center></td>\n",
    "        <td><center>Dimension reduction: detecting 2D structure in 3D data</center></td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "\n",
    "\n",
    "This course will only introduce concepts and methods from **supervised learning**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to apply machine learning in practice?\n",
    "\n",
    "Application of machine learning in practice consists of several phases:\n",
    "\n",
    "1. Understand and clean your data.\n",
    "1. Learn / train a model \n",
    "2. Analyze model for its quality / performance\n",
    "2. Apply this model to new incoming data\n",
    "\n",
    "In practice steps 1. and 2. are iterated for different machine learning algorithms with different configurations until performance is optimal or sufficient. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise section 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "<strong>TODO:</strong> transform to az set of small exercises (instead of a tutorial/example as it is now).\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our example beer data set reflects the very personal opinion of one of the tutors which beer he likes and which not. To learn a predictive model and to understand influential factors all beers went through some lab analysis to measure alcohol content, bitterness, darkness and fruitiness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Load the data and show the overall structure using `pandas`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(225, 5)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# read some data\n",
    "beer_data = pd.read_csv(\"beers.csv\")\n",
    "print(beer_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol_content</th>\n",
       "      <th>bitterness</th>\n",
       "      <th>darkness</th>\n",
       "      <th>fruitiness</th>\n",
       "      <th>is_yummy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.739295</td>\n",
       "      <td>0.422503</td>\n",
       "      <td>0.989463</td>\n",
       "      <td>0.215791</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.207849</td>\n",
       "      <td>0.841668</td>\n",
       "      <td>0.928626</td>\n",
       "      <td>0.380420</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.709494</td>\n",
       "      <td>0.322037</td>\n",
       "      <td>5.374682</td>\n",
       "      <td>0.145231</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.684743</td>\n",
       "      <td>0.434315</td>\n",
       "      <td>4.072805</td>\n",
       "      <td>0.191321</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.148710</td>\n",
       "      <td>0.570586</td>\n",
       "      <td>1.461568</td>\n",
       "      <td>0.260218</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol_content  bitterness  darkness  fruitiness  is_yummy\n",
       "0         3.739295    0.422503  0.989463    0.215791         0\n",
       "1         4.207849    0.841668  0.928626    0.380420         0\n",
       "2         4.709494    0.322037  5.374682    0.145231         1\n",
       "3         4.684743    0.434315  4.072805    0.191321         1\n",
       "4         4.148710    0.570586  1.461568    0.260218         0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show first 5 rows\n",
    "beer_data.head(5)\n",
    "\n",
    "# there is alos beer_data.tail(5) !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol_content</th>\n",
       "      <th>bitterness</th>\n",
       "      <th>darkness</th>\n",
       "      <th>fruitiness</th>\n",
       "      <th>is_yummy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>225.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>225.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.711873</td>\n",
       "      <td>0.463945</td>\n",
       "      <td>2.574963</td>\n",
       "      <td>0.223111</td>\n",
       "      <td>0.528889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.437040</td>\n",
       "      <td>0.227366</td>\n",
       "      <td>1.725916</td>\n",
       "      <td>0.117272</td>\n",
       "      <td>0.500278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.073993</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.429183</td>\n",
       "      <td>0.281291</td>\n",
       "      <td>1.197640</td>\n",
       "      <td>0.135783</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.740846</td>\n",
       "      <td>0.488249</td>\n",
       "      <td>2.026548</td>\n",
       "      <td>0.242396</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.005170</td>\n",
       "      <td>0.631056</td>\n",
       "      <td>4.043995</td>\n",
       "      <td>0.311874</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.955272</td>\n",
       "      <td>1.080170</td>\n",
       "      <td>7.221285</td>\n",
       "      <td>0.535315</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       alcohol_content  bitterness    darkness  fruitiness    is_yummy\n",
       "count       225.000000  225.000000  225.000000  225.000000  225.000000\n",
       "mean          4.711873    0.463945    2.574963    0.223111    0.528889\n",
       "std           0.437040    0.227366    1.725916    0.117272    0.500278\n",
       "min           3.073993    0.000000    0.000000    0.000000    0.000000\n",
       "25%           4.429183    0.281291    1.197640    0.135783    0.000000\n",
       "50%           4.740846    0.488249    2.026548    0.242396    1.000000\n",
       "75%           5.005170    0.631056    4.043995    0.311874    1.000000\n",
       "max           5.955272    1.080170    7.221285    0.535315    1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show basic statistics of the data\n",
    "beer_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Visualy inspect data using `seaborn`\n",
    "\n",
    "Such checks are very useful before you start throwning ML on your data. Some vague understanding how features are distributed and correlate can later be very helpfull to optimize performance of ML procedures.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 776.6x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],