0 - Python Intro.ipynb

{
 "metadata": {
  "name": "0 - Python Intro"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Python\n",
      "======"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(\"Hello World\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Hello World\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = 1\n",
      "y = x + 2\n",
      "print x, y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 3\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Libraries\n",
      "=========\n",
      "NumPy\n",
      "-----\n",
      "\n",
      "The **NumPy** library provides multi-dimensional arrays (e.g. vectors, matrices, ...)\n",
      "\n",
      "NumPy is usually referred to using the shorthand module name `np`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "v = np.zeros(3)\n",
      "M = np.ones((3,4))\n",
      "A = np.array([[1,2,3],[4,5,6]]) \n",
      "print \"v\\n\", v\n",
      "print \"M\\n\", M\n",
      "print \"A\\n\", A"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "v\n",
        "[ 0.  0.  0.]\n",
        "M\n",
        "[[ 1.  1.  1.  1.]\n",
        " [ 1.  1.  1.  1.]\n",
        " [ 1.  1.  1.  1.]]\n",
        "A\n",
        "[[1 2 3]\n",
        " [4 5 6]]\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can also work with sub-matrices in a similar fashion as in MatLab, but starting with index 0:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print A\n",
      "print \"the first column of A:\", A[:,0]\n",
      "print \"the first    row of A:\", A[0,:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[1 2 3]\n",
        " [4 5 6]]\n",
        "the first column of A: [1 4]\n",
        "the first    row of A: [1 2 3]\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.arange(10)\n",
      "print x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0 1 2 3 4 5 6 7 8 9]\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = np.random.uniform(size=10) # note the named argument here. The 1st (default) arguments are 0 and 1.\n",
      "y # last element in cell is printed automatically"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "array([ 0.552715  ,  0.73310284,  0.25826838,  0.04512618,  0.57048334,\n",
        "        0.54865524,  0.93093535,  0.72744663,  0.96537373,  0.69774996])"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Matplotlib\n",
      "----------\n",
      "\n",
      "This is a library used to plot graphs conveniently directly from Python. Many graph types are supported.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "HTML('<iframe src=http://matplotlib.org/gallery.html width=700 height=350></iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=http://matplotlib.org/gallery.html width=700 height=350></iframe>"
       ],
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<IPython.core.display.HTML at 0xad28dac>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(x, y)\n",
      "plt.title(\"Sample Plot\")\n",
      "plt.xlabel(\"x label\")\n",
      "plt.ylabel(\"y label\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEVCAYAAAAGrllxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZMaYBIiIzBiTABGRGfv/sSfAWKKFpu4AAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(x, y, s=100) # press TAB after opening parenthesis to see help on `scatter`;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD9CAYAAAC/fMwDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDyxuTuz/FLNZAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Scikit-Learn"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "HTML('<iframe src=http://scikit-learn.sourceforge.net/dev/auto_examples/index.html width=900 height=350></iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=http://scikit-learn.sourceforge.net/dev/auto_examples/index.html width=900 height=350></iframe>"
       ],
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<IPython.core.display.HTML at 0xae470ec>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Easy-to use machine-learning with many examples and documentation.\n",
      "\n",
      "\n",
      "Classification\n",
      "--------------\n",
      "- **Logistic Regression**\n",
      "- Support Vector Machines\n",
      "- Perceptron\n",
      "- Decision Trees\n",
      "- Random Forests\n",
      "- Naive Bayes\n",
      "- **Neares Neighbors**\n",
      "- ...\n",
      "\n",
      "Regression\n",
      "----------\n",
      "- **Linear Regression**\n",
      "- Lasso\n",
      "- Lars\n",
      "- Ridge Regression\n",
      "- Support Vector Regression\n",
      "- Regression Forests\n",
      "- ...\n",
      "\n",
      "Feature Extraction\n",
      "------------------\n",
      "- **Principal Component Analysis**\n",
      "- Independend Component Analysis\n",
      "- Nonnegative Matrix Factorization\n",
      "- Sparse Decomposition\n",
      "- Locally Linear Embedding\n",
      "- KernelPCA\n",
      "- ...\n",
      "\n",
      "Clustering\n",
      "----------\n",
      "- **$k$-Means**\n",
      "- Mean Shift\n",
      "- Spectral Clustering\n",
      "- Affinity Propagation\n",
      "- DBSCAN\n",
      "- ...\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}