mirror of
https://github.com/gsi-upm/sitc
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115 lines
3.7 KiB
Plaintext
115 lines
3.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"![](images/EscUpmPolit_p.gif \"UPM\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Course Notes for Learning Intelligent Systems"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Introduction to Machine Learning II\n",
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" \n",
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"In this lab session, we will go deeper in some aspects that were introduced in the previous session. This time we will delve into a little bit more detail about reading datasets, analyzing data and selecting features. In addition, we will explore the machine learning algorithm SVM in a binary classification problem provided by the Titanic dataset.\n",
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"\n",
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"# Objectives\n",
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"\n",
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"In this lecture we are going to introduce some more details about machine learning aspects. \n",
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"\n",
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"The main objectives of this session are:\n",
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"* Learn how to read data from a file or URL with pandas\n",
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"* Learn how to use the pandas DataFrame data structure\n",
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"* Learn how to select features\n",
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"* Understand better and SVM machine learning algorithm"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Table of Contents"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"1. [Home](3_0_0_Intro_ML_2.ipynb)\n",
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"1. [The Titanic Dataset. Reading Data](3_1_Read_Data.ipynb)\n",
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"1. [Introduction to Pandas](3_2_Pandas.ipynb)\n",
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"1. [Preprocessing: Data Munging with DataFrames](3_3_Data_Munging_with_Pandas.ipynb)\n",
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"2. [Preprocessing: Visualisation and for DataFrames](3_4_Visualisation_Pandas.ipynb)\n",
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"3. [Exercise 1](3_5_Exercise_1.ipynb)\n",
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"1. [Machine Learning](3_6_Machine_Learning.ipynb)\n",
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" 1. [SVM](3_7_SVM.ipynb)\n",
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"5. [Exercise 2](3_8_Exercise_2.ipynb)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## References"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"* [IPython Notebook Tutorial for Titanic: Machine Learning from Disaster](https://www.kaggle.com/c/titanic/forums/t/5105/ipython-notebook-tutorial-for-titanic-machine-learning-from-disaster)\n",
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"* [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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"* [Learning scikit-learn: Machine Learning in Python](http://proquest.safaribooksonline.com/book/programming/python/9781783281930/1dot-machine-learning-a-gentle-introduction/ch01s02_html), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2013.\n",
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"* [Python Machine Learning](http://proquest.safaribooksonline.com/book/programming/python/9781783555130), Sebastian Raschka, Packt Publishing, 2015."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Licence\n",
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"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
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"\n",
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"© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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