sitc/ml1/2_0_1_Objectives.ipynb

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    "![](files/images/EscUpmPolit_p.gif \"UPM\")"
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   "source": [
    "# Course Notes for Learning Intelligent Systems"
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   "source": [
    "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, ©  Carlos A. Iglesias"
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   "source": [
    "## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)"
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    "# Introduction to Machine Learning\n",
    "\n",
    "This lecture provides a quick introduction to Machine Learning in Python using the Iris dataset as an example. In this session we will focus on applying multiclass classification algorithms.\n",
    "\n",
    "The main objectives of this session are:\n",
    "\n",
    "* Learn to use scikit-learn\n",
    "* Learn the basic steps to apply machine learning techniques: dataset analysis, load, preprocessing, training, validation, optimization and persistence.\n",
    "* Learn how to do a exploratory data analysis\n",
    "* Learn how to visualise a dataset\n",
    "* Learn how to load a bundled dataset\n",
    "* Learn how to separate the dataset into traning and testing datasets\n",
    "* Learn how to train a classifier\n",
    "* Learn how to predict with a trained classifier\n",
    "* Learn how to evaluate the predictions\n",
    "* Learn how to optimize the configuration of a classifier\n",
    "* Learn how to save a model\n"
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   "source": [
    "## References"
   ]
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   "source": [
    "* [Scikit-learn web page](http://scikit-learn.org/stable/)\n",
    "* [Scikit-learn videos](http://blog.kaggle.com/author/kevin-markham/) and [notebooks](https://github.com/justmarkham/scikit-learn-videos) by Kevin Marham\n"
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   "source": [
    "## LIcence\n",
    "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
    "\n",
    "©  Carlos A. Iglesias, Universidad Politécnica de Madrid."
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