{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c50398ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aaf49431",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9 0 8]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "a = np.array([[9,0,8],[7,5,4],[4,5,3],[1,2,3],[2,3,4]])\n",
    "print(a[0])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "106b79cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ed3a7910",
   "metadata": {},
   "source": [
    "# Welcome to your notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1f632132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello world, I am learning ML\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello world, I am learning ML\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
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