sitc/ml1/2_0_0_Intro_ML.ipynb

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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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    "# Introduction to Machine Learning\n",
    "\n",
    "This lecture provides a quick introduction to Machine Learning in Python using the Iris dataset as an example. \n",
    "\n",
    "In this session we will focus on applying multiclass classification algorithms."
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   "metadata": {},
   "source": [
    "# Table of Contents"
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   "source": [
    "1. [Home](2_0_0_Intro_ML.ipynb)\n",
    "    1. [Objectives](2_0_1_Objectives.ipynb)\n",
    "1. [What is scikit-learn](2_1_Intro_ScikitLearn.ipynb)\n",
    "1. [Reading Data](2_2_Read_Data.ipynb)\n",
    "2. [Visualisation](2_3_0_Visualisation.ipynb)\n",
    "    1. [Advanced visualisation](2_3_1_Advanced_Visualisation.ipynb)\n",
    "3. [Preprocessing](2_4_Preprocessing.ipynb)\n",
    "4. [Machine learning](2_5_0_Machine_Learning.ipynb)\n",
    "   1. [kNN Model](2_5_1_kNN_Model.ipynb)\n",
    "   1. [Decision Tree Learning Model](2_5_2_Decision_Tree_Model.ipynb)\n",
    "4. [Model tuning](2_6_Model_Tuning.ipynb)\n",
    "4. [Model persistence](2_7_Model_Persistence.ipynb)\n",
    "4. [Conclusions](2_8_Conclusions.ipynb)"
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   "source": [
    "## References"
   ]
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    "* [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",
    "* [Python Machine Learning](https://learning.oreilly.com/library/view/python-machine-learning/9781789955750/), Sebastian Raschka, Packt Publishing, 2019."
   ]
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   "metadata": {},
   "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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