Assignment (Week 1 and 2).ipynb

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    "# `Note:` All assignment should be done inside the notebook (Double tap on each of this text to edit)."
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   "source": [
    "### `Question 1`: Chika Nwogu-Agbakuru   \n",
    "### 18100111137"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `Question 2`: Natural Language Processing is how the computers understands and manipulate the human language."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `Question 3`: \n",
    "- Machine translation\n",
    "- Sentiment Analysis\n",
    "- Advertising\n",
    "- Healthcare\n",
    "- Conversational AI\n",
    "- Recruiting"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `Question 4`: One challenge is Ambiguity. This is when sentences and phrases that potentially have two or more possible interpretations. Types of Ambiguity are Lexical, Syntactic and Referential Ambiguity. "
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'I', 'am', 'and', 'assignment', 'doing', 'listening', 'music', 'my', 'to'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "### `Question 5`: \n",
    "\n",
    "text1 = \"I am doing my assignment and I am listening to music\"\n",
    "\n",
    "text2 = text1.split(\" \")\n",
    "set(text2)"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">>>> # Have a wonderful week ahead."
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "text3 = text1.replace(\"o\",\"u\")"
   ]
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   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'I am duing my assignment and I am listening tu music'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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    "text3"
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   "execution_count": 9,
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   "source": [
    "text4 = text1.istitle()"
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   "execution_count": 10,
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    {
     "data": {
      "text/plain": [
       "False"
      ]
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "text4"
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   "execution_count": null,
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   "source": []
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