Introduction to Machine Learning with Scikit Learn: Setup

Software Requirements

You will need a terminal, Python 3.8+, and the ability to create Python virtual environments.

Optional

It’s recommended to have a code editor or Integrated Development Environment. Examples include Spyder, VSCode/Visual Studio Code or PyCharm. Visual Studio Code is lightweight and easy to install, and available on Window, Mac and Linux.

Setup

Create a new directory for the workshop, then launch a terminal in it:

mkdir workshop-ml
cd workshop-ml

Creating a new Virtual Environment

You will need the Numpy, Pandas, MatPlotLib, Seaborn and OpenCV packages. We’ll install these prerequisites in a virtual environment, to prevent them from cluttering up your Python environment or conflicting with any specific versions you have installed already.

To create a new virtual environment for the project, open the terminal and type:

python3 -m venv venv

Missing Module?

If you’re on Linux and this doesn’t work, try installing python3-venv using your package manager, e.g. sudo apt-get install python3-venv.

Installing your prerequisites

Activate your virtual environment, and install the prerequisites:

source venv/bin/activate
pip install numpy pandas matplotlib seaborn scikit-learn opencv-python

You’ll need to activate the environment again to use it at the start of the lesson.