Not done reviewing ml2 yet

ml2/3_0_0_Intro_ML_2.ipynb

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+    "![](images/EscUpmPolit_p.gif \"UPM\")"
+   ]
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+  {
+   "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 two additional machine learning algorithms: perceptron and 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 the Perceptron and SVM machine learning algorithms"
+   ]
+  },
+  {
+   "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."
+   ]
+  }
+ ],
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