ML-For-Beginners/2-Regression/1-Tools

Introduction to Machine Learning

Add a sketchnote if possible/appropriate

Pre-lecture quiz

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 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 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 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 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. 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

Review & Self Study

Assignment: Assignment Name