# Introduction to Machine Learning

Add a sketchnote if possible/appropriate

![Embed a video here if available](video-url)

## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)

Describe what we will learn

### Introduction

In this lesson, you will learn:
- 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

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.

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.

> 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

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`.

Notebooks are an interactive environment that allow the developer to both code and add notes and documentation around the code.
### Work with Your Notebook

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.

In your notebook, add a comment. To do this, click the 'md' icon and add a bit of markdown, like `# My First Notebook`.

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 - think for a minute how different a web developer's working environment is versus that of a data scientist.

## 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

Optional: add a screenshot of the completed lesson's UI if appropriate

## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)

## Review & Self Study

**Assignment**: [Assignment Name](assignment.md)