mirror of
https://github.com/gsi-upm/sitc
synced 2024-11-17 20:12:28 +00:00
115 lines
3.7 KiB
Plaintext
115 lines
3.7 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"![](images/EscUpmPolit_p.gif \"UPM\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Course Notes for Learning Intelligent Systems"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Introduction to Machine Learning II\n",
|
|
" \n",
|
|
"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, analysing 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",
|
|
"\n",
|
|
"# Objectives\n",
|
|
"\n",
|
|
"In this lecture we are going to introduce some more details about machine learning aspects. \n",
|
|
"\n",
|
|
"The main objectives of this session are:\n",
|
|
"* Learn how to read data from a file or URL with pandas\n",
|
|
"* Learn how to use the pandas DataFrame data structure\n",
|
|
"* Learn how to select features\n",
|
|
"* Understand better and SVM machine learning algorithm"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Table of Contents"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"1. [Home](3_0_0_Intro_ML_2.ipynb)\n",
|
|
"1. [The Titanic Dataset. Reading Data](3_1_Read_Data.ipynb)\n",
|
|
"1. [Introduction to Pandas](3_2_Pandas.ipynb)\n",
|
|
"1. [Preprocessing: Data Munging with DataFrames](3_3_Data_Munging_with_Pandas.ipynb)\n",
|
|
"2. [Preprocessing: Visualisation and for DataFrames](3_4_Visualisation_Pandas.ipynb)\n",
|
|
"3. [Exercise 1](3_5_Exercise_1.ipynb)\n",
|
|
"1. [Machine Learning](3_6_Machine_Learning.ipynb)\n",
|
|
" 1. [SVM](3_7_SVM.ipynb)\n",
|
|
"5. [Exercise 2](3_8_Exercise_2.ipynb)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## References"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"* [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",
|
|
"* [Scikit-learn videos](http://blog.kaggle.com/author/kevin-markham/) and [notebooks](https://github.com/justmarkham/scikit-learn-videos) by Kevin Marham\n",
|
|
"* [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",
|
|
"* [Python Machine Learning](http://proquest.safaribooksonline.com/book/programming/python/9781783555130), Sebastian Raschka, Packt Publishing, 2015."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"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",
|
|
"© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.5.1"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|