pkumarreddy

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    "# Title : Applying Different Types of classification Algorithms on Iris Data"
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pkumarreddy

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pkumarreddy

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pkumarreddy

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pkumarreddy

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pkumarreddy

{ "cells": [  {   "cell_type": "code",   "execution_count": 1,   "metadata": {    "collapsed": true   },   "outputs": [],   "source": [    "# from IPython.display import YouTubeVideo\n",    "# YouTubeVideo('2lpS6gUwiJQ')"   ]  },  {   "cell_type": "code",   "execution_count": 2,   "metadata": {    "collapsed": false   },   "outputs": [    {     "data": {      "text/html": [       "<div>\n",       "<table border=\"1\" class=\"dataframe\">\n",       "  <thead>\n",       "    <tr style=\"text-align: right;\">\n",       "      <th></th>\n",       "      <th>user</th>\n",       "      <th>0</th>\n",       "      <th>1</th>\n",       "      <th>2</th>\n",       "      <th>3</th>\n",       "      <th>4</th>\n",       "      <th>5</th>\n",       "      <th>6</th>\n",       "      <th>7</th>\n",       "      <th>8</th>\n",       "      <th>...</th>\n",       "      <th>15</th>\n",       "      <th>16</th>\n",       "      <th>17</th>\n",       "      <th>18</th>\n",       "      <th>19</th>\n",       "      <th>20</th

pkumarreddy

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pkumarreddy

{ "cells": [  {   "cell_type": "code",   "execution_count": 1,   "metadata": {    "collapsed": false   },   "outputs": [],   "source": [    "#from IPython.display import YouTubeVideo\n",    "#YouTubeVideo('2lpS6gUwiJQ')"   ]  },  {   "cell_type": "markdown",   "metadata": {},   "source": [    "## This talk\n",    "### •A different way to look at graph analysis and visualization,\n",    "### •as an introduction to a few cool algorithms: Truncated SVD, K-Means and t-SNE\n",    "### •with a practical walkthrough using scikit-learn and friends numpy and bokeh,\n",    "### •and finishing off with some more general commentary on this approach to data analysis\n"   ]  },  {   "cell_type": "markdown",   "metadata": {},   "source": [    "## A map of Reddit¶\n",    "###     •Reddit is \"the front page of the internet\"\n",    "###     •Basically a discussion board, with sub-boards called subreddits \n",    "###     •Figure from this paper: Navigating the massive world of reddit: Using backbone networks to map user #int

pkumarreddy

{ "cells": [  {   "cell_type": "code",   "execution_count": 1,   "metadata": {    "collapsed": false   },   "outputs": [],   "source": [    "#from IPython.display import YouTubeVideo\n",    "#YouTubeVideo('2lpS6gUwiJQ')"   ]  },  {   "cell_type": "markdown",   "metadata": {},   "source": [    "## This talk\n",    "### •A different way to look at graph analysis and visualization,\n",    "### •as an introduction to a few cool algorithms: Truncated SVD, K-Means and t-SNE\n",    "### •with a practical walkthrough using scikit-learn and friends numpy and bokeh,\n",    "### •and finishing off with some more general commentary on this approach to data analysis\n"   ]  },  {   "cell_type": "markdown",   "metadata": {},   "source": [    "## A map of Reddit¶\n",    "###     •Reddit is \"the front page of the internet\"\n",    "###     •Basically a discussion board, with sub-boards called subreddits \n",    "###     •Figure from this paper: Navigating the massive world of reddit: Using backbone networks to map user #int

pkumarreddy

{ "cells": [  {   "cell_type": "markdown",   "metadata": {},   "source": [    "\n",    "## Data Science and (Unsupervised) Machine Learning with scikit-learn \n",    "\n",    "###By kumar Reddy \n",    "### Presented Jan 04, 2017 "   ]  },  {   "cell_type": "code",   "execution_count": 1,   "metadata": {    "collapsed": false   },   "outputs": [    {     "data": {      "text/html": [       "\n",       "        <iframe\n",       "            width=\"400\"\n",       "            height=\"300\"\n",       "            src=\"https://www.youtube.com/embed/2lpS6gUwiJQ\"\n",       "            frameborder=\"0\"\n",       "            allowfullscreen\n",       "        ></iframe>\n",       "        "      ],      "text/plain": [       "<IPython.lib.display.YouTubeVideo at 0x3b61cc0>"      ]     },     "execution_count": 1,     "metadata": {},     "output_type": "execute_result"    }   ],   "source": [    "from IPython.display import YouTubeVideo\n",    "YouTubeVideo('2lpS6gUwiJQ')"   ]  },  {   "cell_type": "markdown"