committing my hello_worlds

app/hello_world.ipynb

@@ -0,0 +1,68 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(0)\n",
+    "\n",
+    "values = np.random.randn(100) # array of normally distributed random numbers\n",
+    "s = pd.Series(values) # generate a pandas series\n",
+    "s.plot(kind='hist', title='Normally distributed random values') # hist computes distribution\n",
+    "plt.show()   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "llmops-course",
+   "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": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

app/hello_world.py

@@ -0,0 +1 @@
+print("hello world! let's do some ml ops!")