Added visualization notebooks

ml21/visualization/00_Intro_Visualization.ipynb

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+{
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+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "![](images/EscUpmPolit_p.gif \"UPM\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "# Course Notes for Learning Intelligent Systems"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Introduction to Visualization\n",
+    " \n",
+    "In this session, we will get more insight regarding how to visualize data.\n",
+    "\n",
+    "# Objectives\n",
+    "\n",
+    "The main objectives of this session are:\n",
+    "* Understanding how to visualize data\n",
+    "* Understanding the purpose of different charts \n",
+    "* Experimenting with several environments for visualizing data\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Seaborn\n",
+    "\n",
+    "Seaborn is a library that visualizes data in Python. The main characteristics are:\n",
+    "\n",
+    "* A dataset-oriented API for examining relationships between multiple variables\n",
+    "* Specialized support for using categorical variables to show observations or aggregate statistics\n",
+    "* Options for visualizing univariate or bivariate distributions and for comparing them between subsets of data\n",
+    "* Automatic estimation and plotting of linear regression models for different kinds of dependent variables\n",
+    "* Convenient views of the overall structure of complex datasets\n",
+    "* High-level abstractions for structuring multi-plot grids that let you quickly build complex visualizations\n",
+    "* Concise control over matplotlib figure styling with several built-in themes\n",
+    "* Tools for choosing color palettes that faithfully reveal patterns in your data\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Install\n",
+    "Use:\n",
+    "\n",
+    "**conda install seaborn**\n",
+    "\n",
+    "or \n",
+    "\n",
+    "**pip install seaborn**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Table of Contents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "1. [Home](00_Intro_Visualization.ipynb)\n",
+    "2. [Dataset](01_Dataset.ipynb)\n",
+    "3. [Comparison Charts](02_Comparison_Charts.ipynb)\n",
+    "     1. [More Comparison Charts](02_01_More_Comparison_Charts.ipynb)\n",
+    "4. [Distribution Charts](03_Distribution_Charts.ipynb)\n",
+    "5. [Hierarchical charts](04_Hierarchical_Charts.ipynb)\n",
+    "6. [Relational charts](05_Relational_Charts.ipynb)\n",
+    "7. [Spatial charts](06_Spatial_Charts.ipynb)\n",
+    "8. [Temporal charts](07_Temporal_Charts.ipynb)"
+   ]
+  },
+  {
+   "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."
+   ]
+  }
+ ],
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+    "top": "50px"
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+   "window_display": false
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
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