Stanford Online/ DeepLearning.AI. Supervised Machine Learaning: Regression and Classification, Jupyter Notebooks.

C1_W1_Lab01_Python_Jupyter_Soln.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optional Lab:  Brief Introduction to Python and Jupyter Notebooks\n",
    "Welcome to the first optional lab! \n",
    "Optional labs are available to:\n",
    "- provide information - like this notebook\n",
    "- reinforce lecture material with hands-on examples\n",
    "- provide working examples of routines used in the graded labs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goals\n",
    "In this lab, you will:\n",
    "- Get a brief introduction to Jupyter notebooks\n",
    "- Take a tour of Jupyter notebooks\n",
    "- Learn the difference between markdown cells and code cells\n",
    "- Practice some basic python\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The easiest way to become familiar with Jupyter notebooks is to take the tour available above in the Help menu:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "    <center> <img src=\"./images/C1W1L1_Tour.PNG\"  alt='missing' width=\"400\"  ><center/>\n",
    "<figure/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter notebooks have two types of cells that are used in this course. Cells such as this which contain documentation called `Markdown Cells`. The name is derived from the simple formatting language used in the cells. You will not be required to produce markdown cells. Its useful to understand the `cell pulldown` shown in graphic below. Occasionally, a cell will end up in the wrong mode and you may need to restore it to the right state:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "   <img src=\"./images/C1W1L1_Markdown.PNG\"  alt='missing' width=\"400\"  >\n",
    "<figure/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The other type of cell is the `code cell` where you will write your code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is  code cell\n"
     ]
    }
   ],
   "source": [
    "#This is  a 'Code' Cell\n",
    "print(\"This is  code cell\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python\n",
    "You can write your code in the code cells. \n",
    "To run the code, select the cell and either\n",
    "- hold the shift-key down and hit 'enter' or 'return'\n",
    "- click the 'run' arrow above\n",
    "<figure>\n",
    "    <img src=\"./images/C1W1L1_Run.PNG\"  width=\"400\"  >\n",
    "<figure/>\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print statement\n",
    "Print statements will generally use the python f-string style.  \n",
    "Try creating your own print in the following cell.  \n",
    "Try both methods of running the cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f strings allow you to embed variables right in the strings!\n"
     ]
    }
   ],
   "source": [
    "# print statements\n",
    "variable = \"right in the strings!\"\n",
    "print(f\"f strings allow you to embed variables {variable}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Congratulations!\n",
    "You now know how to find your way around a Jupyter Notebook."
   ]
  }
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