Summary and Schedule

This workshop provides a beginner-friendly overview of machine learning (ML) and common ML methods— including regression, classification, clustering, dimensionality reduction, ensemble methods, and a quick neural-network demo—using Python + scikit-learn. The broad coverage is designed to jump-start your ML journey and point you toward next learning steps.

Prerequisites

A basic understanding of Python. You will need to know how to write a for loop, if statement, use functions, libraries and perform basic arithmetic.

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.

Overview

Questions

  • How do I prepare my system for this workshop?
  • How do I create and activate a Python virtual environment with uv?
  • How do I make my workshop environment available in JupyterLab?

Objectives

  • Create a dedicated folder for the workshop.
  • Install Python 3.11.9.
  • Install uv and use it to create and manage a virtual environment.
  • Configure JupyterLab to use the workshop environment.

Setup

Please complete the setup at least a day in advance of the workshop. If you run into issues, contact the workshop organizers by email so you’re ready to begin on time. The setup steps include:

  1. Set up the workshop folder
  2. Install Python 3.11.9
  3. Install uv and set up the virtual environment

0. Set up a terminal or Git Bash if on Windows

To run the commands referenced in this setup, we recommend using:

  • Terminal (Mac, Linux)
  • Git Bash (Windows). Download and install from here: https://git-scm.com/install/windows. Git Bash provides a shell environment that closely resembles the terminal.

1. Set up the workshop folder

Create a folder on your desktop called ML_workshop for storing the workshop data and environment.

Open a terminal or Git Bash (Windows) to run the following commands. If you prefer, you can also create the folder manually on your Desktop.

BASH 

cd ~/Desktop
mkdir ML_workshop
cd ML_workshop
pwd

2. Install Python 3.11.9

We recommend using Python 3.11.9 to ensure consistency across participants.

Download the appropriate installer from the official 3.11.9 downloads page. Follow your OS-specific installation steps. When prompted, select “Add python.exe to PATH”.

3. Install uv and set up the environment

uv is a modern, fast Python package and environment manager. It’s significantly faster than pip and simplifies reproducible setup.

A. Install uv

BASH 

pip install uv

B. Create a requirements.txt file in the ML_workshop folder

BASH 

touch requirements.txt

Use your preferred text editor to add the following contents into ML_workshop/requirements.txt. Make sure to save the file after your edits.

numpy
pandas
matplotlib
opencv-python
scikit-learn
jupyterlab
seaborn

C. Set up the virtual environment

BASH 

pwd  # check to make sure you're still in the ML_workshop folder.

Create the virtual environment using Python 3.11.9:

BASH 

uv venv --python=3.11.9
Callout

What does the .venv folder contain?

Running uv venv --python=3.11.9 creates a folder named .venv/ in your ML_workshop directory.

Inside this folder, you’ll find multiple subfolders and files:

  • bin/ (or Scripts/ on Windows): contains the Python interpreter and executable scripts
  • lib/: stores all installed Python packages (and their dependencies)
  • pyvenv.cfg: tracks Python version and configuration
  • include/: headers used to build native extensions

These components form an isolated environment, keeping your installed packages separate from your global Python setup.

D. Activate the environment

Run one of the OS-specific commands below:

BASH 

# Mac/Linux
source .venv/bin/activate

# Git Bash on Windows
source .venv/Scripts/activate

# Windows CMD (not recommended)
.venv\Scripts\activate.bat

E. Install requirements.txt

BASH 

uv pip install -r requirements.txt

F. Add the environment to JupyterLab

Run one of the OS-specific commands below.

BASH 

# Mac/Linux
.venv/bin/python -m ipykernel install --user --name=.venv --display-name "ML_workshop"

# Git Bash on Windows
.venv/Scripts/python.exe -m ipykernel install --user --name=.venv --display-name "ML_workshop"

# Windows CMD (not recommended)
.venv\Scripts\python.exe -m ipykernel install --user --name=.venv --display-name "ML_workshop"

G. Launch JupyterLab

BASH 

jupyter lab

When Jupyter opens, select the ML_workshop kernel from the dropdown to start a new notebook.

PYTHON 

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2
import seaborn as sns
from sklearn import datasets, model_selection, metrics

If this runs without error, your setup is complete!

Key Points
  • Use a dedicated ML_workshop folder to keep all materials and the environment together.
  • Install and use Python 3.11.9 so your setup matches the instructors’.
  • Use uv to create and manage an isolated virtual environment in .venv/.
  • Register the environment as a Jupyter kernel and select it before working through the lesson.