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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Lesson Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this you will learn all about the following:\n",
      "\n",
      "- Platform: AWS\n",
      "- Programming language: Python\n",
      "- IDE: IPython Notebooks\n",
      "- Libraries: Numpy, Pandas\n",
      "- Administration and Monitoring with Ajenti"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Amazon Web Services"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "AWS is a cloud computing service offering a range of managed and unmanaged services. We will be working with EC2 which is commonly used for performing \"Machine Learning\" processing in production environment.\n",
      "\n",
      "AWS EC2 offers a FREE Trier which is what we will be working with. So if you don't have an AWS account yet, please create one.\n",
      "\n",
      "https://aws.amazon.com"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Alternative - Local Setup"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ubuntu (VM)\n",
      "Install:\n",
      "\n",
      "- python-dev\n",
      "- python-pip\n",
      "- python-ipython\n",
      "- python-numpy\n",
      "- python-scipy\n",
      "\n",
      "Start Server:\n",
      "\n",
      "ipython notebook"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Python"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will be coding most of our work in python, so it important to be familiar with the language. One good I found was \"Learn Python the Hard Way\" which is available for free online.\n",
      "\n",
      "http://learnpythonthehardway.org/book/\n",
      "\n",
      "I'll go over some basics that will get you started."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = 10\n",
      "y = 5\n",
      "z = x + y\n",
      "print z"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "word_1 = \"Hello\"\n",
      "word_2 = \"Python\"\n",
      "sentence = word_1 + \" \" + word_2\n",
      "print sentence"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's do some math\n",
      "print x + y\n",
      "print x - y\n",
      "print x * y\n",
      "print x / y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Formating String\n",
      "template = \"Hello %s\"\n",
      "print template % \"Python\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Using string formating to improve math results\n",
      "print \"Add Result: %s\" % (x + y)\n",
      "print \"Subtract Result: %s\" % (x - y)\n",
      "print \"Multiply Result: %s\" % (x * y)\n",
      "print \"Divide Result: %s\" % (x / y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Passing Multiple Parameters to a string\n",
      "template = \"%s %s %s = %s\"\n",
      "print template % (x, \"+\", y, x+y)\n",
      "print template % (x, \"-\", y, x-y)\n",
      "print template % (x, \"*\", y, x*y)\n",
      "print template % (x, \"/\", y, x/y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# List, Tupal and Dictionary\n",
      "numbers_list = [1,2,3,4,5]\n",
      "numbers_tupal = (1,2,3,4,5)\n",
      "numbers_dict = {1:1, 2:2, 3:3, 4:4, 5:5}\n",
      "\n",
      "# List\n",
      "print \"Full List: %s\" % numbers_list\n",
      "print \"List Length: %s\" % len(numbers_list)\n",
      "print \"First Item: %s\" % numbers_list[0]\n",
      "print \"Last Item: %s\" % numbers_list[-1]\n",
      "print \"\"\n",
      "\n",
      "# Tupal\n",
      "print \"Full Tupal: %s\" % str(numbers_tupal)\n",
      "print \"Tupal Length: %s\" % len(numbers_tupal)\n",
      "print \"First Item: %s\" % numbers_tupal[0]\n",
      "print \"Last Item: %s\" % numbers_tupal[-1]\n",
      "print \"\"\n",
      "\n",
      "# Dictionary\n",
      "print \"Full Dictionary: %s\" % str(numbers_dict)\n",
      "print \"Dictionary Length: %s\" % len(numbers_dict)\n",
      "print \"First Item: %s\" % numbers_dict.values()[0]\n",
      "print \"Last Item: %s\" % numbers_dict.values()[-1]\n",
      "print \"\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "IPython Notebooks"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "IPython Notebook will be our Intergrated Development Environment (IDE). It is web-based and support interactive development. This comes in handy once we are working with data and we would develop out logic based on the data and results.\n",
      "\n",
      "In can write code and execute it on the spot and have others access your work to truely work as a team.\n",
      "\n",
      "It also support inline charts and LATEX to visualize your work and results.\n",
      "\n",
      "Let's test LATEX first to see can it can make our functions more readable\n",
      "\n",
      "\n"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Text code for function\n",
      "f(x) = (x ** 2 + 2 * x) / (1-epsilon)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "LATEX code for a function\n",
      "\n",
      "### $f(x) = \\frac{(x ^ 2 + 2x)}{(1-\\epsilon)}$"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Charts"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A good resource for finding chart examples is:\n",
      "\n",
      "http://matplotlib.org/gallery.html"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "X = [1,2,3,4,5]\n",
      "Y = [3,2,5,0,1]\n",
      "plt.plot(X, Y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(X, Y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(X, Y, c=Y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter( X, Y, c=Y, marker=\"*\", s=400)\n",
      "# Complete list of markers\n",
      "# http://matplotlib.org/api/markers_api.html"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "A Teaser"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from mpl_toolkits.mplot3d import axes3d\n",
      "import matplotlib.pyplot as plt\n",
      "from matplotlib import cm\n",
      "\n",
      "fig = plt.figure(figsize=(10,10))\n",
      "ax = fig.gca(projection='3d')\n",
      "X, Y, Z = axes3d.get_test_data(0.05)\n",
      "ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.3)\n",
      "cset = ax.contourf(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm)\n",
      "cset = ax.contourf(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm)\n",
      "cset = ax.contourf(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm)\n",
      "\n",
      "ax.set_xlabel('X')\n",
      "ax.set_xlim(-40, 40)\n",
      "ax.set_ylabel('Y')\n",
      "ax.set_ylim(-40, 40)\n",
      "ax.set_zlabel('Z')\n",
      "ax.set_zlim(-100, 100)\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Numpy and Pandas"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Numpy**: A powerful library for dealing with large amount of numbers. It performs operation much faster than pure python.\n",
      "\n",
      "**Pandas**: Introduces DataSeries and DataFrame which are based on Numpy arrays. These objects provide many out-of-the-box tools to analize and perform complex operations on your data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "array = np.arange(100)\n",
      "array = array.reshape((10,10))\n",
      "df = pd.DataFrame(array)\n",
      "print df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Performs Operations on Complete List"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df[1] - df[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df[1] + df[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df[1] * df[0] - df[3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Read Data From CSV"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can read CSV file located on our system or on the internet."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Upload A File to the System with Ajenti"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can open Ajenti by opening this link:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "\n",
      "input_form = \"\"\"\n",
      "<a id=\"admin_link\" target=\"_blank\" href=\"#\">Ajenti Administration Interface</a>\n",
      "<p>User: root<br> Password: admin</p>\n",
      "\"\"\"\n",
      "\n",
      "javascript = \"\"\"\n",
      "<script type=\"text/Javascript\">\n",
      "document.getElementById('admin_link').href = \"https://\" + window.location.hostname + \":8000\"\n",
      "</script>\n",
      "\"\"\"\n",
      "\n",
      "HTML(input_form + javascript)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "<a id=\"admin_link\" target=\"_blank\" href=\"#\">Ajenti Administration Interface</a>\n",
        "<p>User: root<br> Password: admin</p>\n",
        "\n",
        "<script type=\"text/Javascript\">\n",
        "document.getElementById('admin_link').href = \"https://\" + window.location.hostname + \":8000\"\n",
        "</script>\n"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<IPython.core.display.HTML at 0x7fc6e58037d0>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Read File from Local System"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "csv_data = pd.read_csv(\"data/yourfile.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### End of Lesson 1\n",
      "For questions please leave them on:\n",
      "\n",
      "- <a href=\"https://www.youtube.com/user/roshanRush\">YouTube</a>\n",
      "- <a href=\"https://twitter.com/twistedhardware\">Twitter</a>\n",
      "- <a href=\"https://plus.google.com/+Tcsaroshan/posts\">Google+</a>\n",
      "\n",
      "<a href=\"/notebooks/Lesson 1 - Intoroduction to Machine Learning.ipynb\">Next Lesson - Intoroduction to Machine Learning</a>\n",
      "\n",
      "In the next lesson:\n",
      "- kNN Classification"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}