sitc/ml2/3_0_0_Intro_ML_2.ipynb

{
 "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, ©  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, 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",
    "\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 and notebooks](https://github.com/justmarkham/scikit-learn-videos) by Kevin Marham\n",
    "* [Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits](https://learning.oreilly.com/library/view/hands-on-machine-learning/9781838826048/), Tarek Amr, Packt Publishing, 2020.\n",
    "* [Python Machine Learning](https://learning.oreilly.com/library/view/python-machine-learning/9781789955750/), Sebastian Raschka and Vahid Mirjalili, Packt Publishing, 2019."
   ]
  },
  {
   "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",
    "©  Carlos A. Iglesias, Universidad Politécnica de Madrid."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.12"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}