Machine Learning intro.ipynb

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    "## __Definition__: \n",
    "Machine learning is a field of computer science that gives computers the __ability to learn without being explicitly programmed__.\n",
    "\n",
    "## __Types of problems__:\n",
    "\n",
    "### __1. Supervised learning:__  \n",
    "   The computer is presented with example inputs and their desired outputs, given by a \"teacher\", and the goal is to learn a general rule that __maps inputs to outputs__.\n",
    "\n",
    "   __1.1 Regression problems:__ The outputs are continuous. (e.g. Predict the price of a house, given a set of parameters)\n",
    "\n"
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    "   __1.2 Classification problems:__ The outputs are discrete. (e.g. Predict in which of the four Houses of Hogwarts School of Witchcraft and Wizardry you belong, based on a set of parameters)\n",
    "\n",
    "  "
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    " __Examples of algorithms:__ Support Vector Machine, Decision Trees, Perceptron, Random Forrest, etc.\n",
    "\n",
    "### __2. Unsupervised learning:__ \n",
    "   No labels are given. The algorithm must find __structure in its input__.\n",
    "\n",
    "   __2.1 Clustering:__ Dividing a set of entries into groups based on a distance metric.\n",
    "\n",
    "   "
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    "__2.2 Dimensionality reduction:__ Maps a vector of features to a corresponding vector in a lower-dimensional space with minimum information loss."
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