Vectorizing

Author: keshavsingh4522

Linkedin/NLP/CH03/.ipynb_checkpoints/CountVectorizer-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Vectorizing\n",
+    "<ul>\n",
+    "    <li>\n",
+    "        The process that we use to convert text to a form that Python and a machine learning model can understand is called vectorizing\n",
+    "    </li>\n",
+    "    <li>\n",
+    "        Vectorization is used to speed up the Python code without using loop\n",
+    "    </li>\n",
+    "</ul>\n",
+    "___\n",
+    "\n",
+    "- ### Count Vectorization\n",
+    "Creates a document-term matrix where the entry of each cell will be a count of the number of times that word occurred in that document."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import nltk\n",
+    "import re\n",
+    "import string\n",
+    "import pandas as pd\n",
+    "pd.set_options('.max_')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Linkedin/NLP/CH03/.ipynb_checkpoints/N-Gram-Vectorizer-checkpoint.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}