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sitc/ml1/2_0_0_Intro_ML.ipynb

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"![](files/images/EscUpmPolit_p.gif \"UPM\")"
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"# Course Notes for Learning Intelligent Systems"
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"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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"metadata": {},
"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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"## 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",
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"* [scikit-learn : Machine Learning Simplified](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), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2017.\n",
"* [Python Machine Learning](https://learning.oreilly.com/library/view/python-machine-learning/9781789955750/), Sebastian Raschka, Packt Publishing, 2019."
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]
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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",
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"© Carlos A. Iglesias, Universidad Politécnica de Madrid."
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]
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