lesson 1

2-Regression/1-Tools/README.md

@@ -11,36 +11,36 @@ Describe what we will learn
 ### Introduction
 
 In this lesson, you will learn:
-- How to configure Visual Studio Code for machine learning tasks
+- How to configure your computer for machine learning tasks
 - An introduction to Scikit-Learn, including installation
 - Some tips on working with Python for ML
-
 ### Prerequisite
 
-What steps should have been covered before this lesson?
+1. Ensure that [Python](https://www.python.org/downloads/) is also installed on your computer. You will use Python for many data science and machine learning tasks. Most computer systems already include a Python installation. Some usages of Python, however, require one version of the software, whereas others require a different version. For this reason, it's useful to work within a virtual environment, or VM. There are useful [Python Coding Packs](https://code.visualstudio.com/learn/educators/installers) available for Windows and ready for integration with VS Code.
 
-### Preparation
+2. Make sure you have Visual Studio Code installed on your computer. Follow [these instructions](https://code.visualstudio.com/) for the basic installation. You are going to use Python in Visual Studio Code in this course, so you might want to brush up on how to [configure](https://docs.microsoft.com/en-us/learn/modules/python-install-vscode/) VS Code for Python development.
 
-Preparatory steps to start this lesson
+> Get comfortable with Python by working through this collection of [Learn modules]()
 
----
+3. Install Scikit-Learn (pronounce it `sci` as in `science`). Follow [these instructions](https://scikit-learn.org/stable/install.html). Since you need to ensure that you use Python 3, it's recommended that you use a pip virtual environment.
+### Preparation
 
-[Step through content in blocks]
+You are going to use **notebooks** to develop your Python code and create machine learning models. This type of file is a common tool for data scientists, and they can be identified by their suffix `.ipynb`.
 
-## [Topic 1]
+Notebooks are an interactive environment that allow the developer to both code and add notes and documentation around the code.
+### Work with Your Notebook
 
-### Task:
+In this folder, you will find the file `notebook.ipynb`. If you open it in VS Code, assuming VS Code is properly configured, a Jupyter server will start with Python 3+ started.
 
-Work together to progressively enhance your codebase to build the project with shared code:
+In your notebook, add a comment. To do this, click the 'md' icon and add a bit of markdown, like `# My First Notebook`.
 
-```html
-code blocks
-```
+Next, add some Python code: Type `print('hi')` and click the arrow to run the code. You should see the printed statement, 'hi'.
 
-✅ Knowledge Check - use this moment to stretch students' knowledge with open questions
+✅ Knowledge Check - think for a minute how different a web developer's working environment is versus that of a data scientist.
 
-## [Topic 2]
+## Scikit-Learn
 
+Now that you are comfortable with Jupyter notebooks and Python in your local environment, let's get equally comfortable with scikit-learn.
 ## [Topic 3]
 
 🚀 Challenge: Add a challenge for students to work on collaboratively in class to enhance the project

2-Regression/1-Tools/notebook.ipynb

@@ -0,0 +1,60 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit",
+   "metadata": {
+    "interpreter": {
+     "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "hi"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "hello notebook\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code goes here\n",
+    "print('hello notebook')"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  }
+ ]
+}