Summary and Schedule

Machine learning is a set of techniques that enable computers to use data to improve their performance in a given task. This is similar in concept to how humans learn to make predictions based upon previous experience and knowledge. Machine learning encompasses a wide range of activities, but broadly speaking it can be used to: find trends in a dataset, classify data into groups or categories, make predictions based upon data, and even “learn” how to interact with an environment when provided with goals to achieve.

We will cover the basics of machine learning using the Scikit-Learn Python package, a popular library for machine learning in Python. Scikit-Learn provides a wide range of machine learning algorithms and tools for data preprocessing, model selection, and evaluation.

Prerequisite

Participants will be assumed to have a basic understanding of Python. You should have a basic understanding of how to run Python code in a Jupyter Notebook, and be able to write for loops, conditional statements, use functions, and import libraries.

Prerequisite

You will need to have python installed on your computer, as well as some additional packages. please refer to the setup instructions below for details.

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	Setup Instructions	Download files required for the lesson
Duration: 00h 00m	1. Introduction	What is machine learning? What are some useful machine learning techniques? ::::::
Duration: 00h 40m	2. Supervised methods - Regression	What is supervised learning? What is regression? How can I model data and make predictions using regression methods? ::::::
Duration: 02h 40m	3. Supervised methods - Classification	How can I classify data into known categories? ::::::
Duration: 03h 40m	4. Ensemble methods	What are ensemble methods? What are random forests? How can we stack estimators in sci-kit learn? ::::::
Duration: 05h 40m	5. Unsupervised methods - Clustering	What is unsupervised learning? How can we use clustering to find data points with similar attributes? ::::::
Duration: 06h 40m	6. Unsupervised methods - Dimensionality reduction	How do we apply machine learning techniques to data with higher dimensions? ::::::
Duration: 07h 40m	7. Neural Networks	What are Neural Networks? How can we classify images using a neural network? ::::::
Duration: 08h 30m	8. Ethics and the Implications of Machine Learning	What are the ethical implications of using machine learning in research? ::::::
Duration: 08h 45m	9. Find out more	Where can you find out more about machine learning? ::::::
Duration: 08h 55m	Finish

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

Setup

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Software

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

Callout

Installing Python

Python is a popular language for scientific computing, and a frequent choice for machine learning as well. To install Python, follow the Beginner’s Guide or head straight to the download page.

Please set up your python environment at least a day in advance of the workshop. If you encounter problems with the installation procedure, ask your workshop organizers via e-mail for assistance so you are ready to go as soon as the workshop begins.

Packages

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You will need the Seaborn, MatPlotLib, Pandas, Numpy and OpenCV packages.

Directory Setup

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

BASH

mkdir workshop-ml
cd workshop-ml

Creating a new Virtual Environment

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We’ll install the prerequisites in a virtual environment, to prevent them from cluttering up your Python environment and causing conflicts. First, create a new directory and ent

venv

uv

Python usually comes with a built-in module called venv that allows you to create isolated environments for your projects.

To create a new virtual environment (“venv”) called “intro_ml” for the project, open the terminal (Mac/Linux), Git Bash (Windows) or Anaconda Prompt (Windows), and type one of the below OS-specific options:

BASH

python3 -m venv intro_ml # mac/linux
python -m venv intro_ml # windows

(If you’re on Linux and this doesn’t work, you may need to install venv first. Try running sudo apt-get install python3-venv first, then python3 -m venv intro_ml)

To activate the environment, run the following OS-specific commands in Terminal (Mac/Linux) or Git Bash (Windows) or Anaconda Prompt (Windows):

• Windows + Git Bash: source intro_ml/Scripts/activate
• Windows + Anaconda Prompt: intro_ml/Scripts/activate
• Mac/Linux: source intro_ml/bin/activate

With the environment activated, install the prerequisites:

BASH

pip install numpy pandas matplotlib seaborn opencv-python scikit-learn
pip install jupyterlab # optional

UV is a package manager that allows you to easily create and manage virtual environments. It is a great alternative to venv, pip, and conda for managing Python environments and packages. It is a newer tool, written in Rust, and is designed to be fast and efficient.

To install UV, run the following command in your terminal:

BASH

pip install uv

To create a new virtual environment in your current directory, run:

BASH

uv venv

This will create a new virtual environment in a directory called .venv in your current directory.

You should see a message indicating that the virtual environment has been created successfully, and a command to activate it (this command will vary depending on your operating system):

BASH

Using CPython 3.12.6 interpreter at: C:\python\3.12.6\python.exe
Creating virtual environment at: .venv
Activate with: source .venv/Scripts/activate

Run the indicated command to activate the virtual environment. You should now see the name of the virtual environment in your terminal prompt in parentheses, indicating that it is active:

BASH

user@my_device MINGW64 /c/Users/user/Documents/intro_ml
$ source .venv/Scripts/activate
(intro_ml)

Once the environment is activated, you can install the required packages using:

BASH

uv pip install numpy pandas matplotlib seaborn opencv-python scikit-learn
uv pip install jupyterlab # optional

Deactivating/activating environment

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To deactivate your virtual environment, simply run deactivate in your terminal or prompt. If you close the terminal, Git Bash, or Conda Prompt without deactivating, the environment will automatically close as the session ends. Later, you can reactivate the environment using the “Activate environment” instructions above to continue working. If you want to keep coding in the same terminal but no longer need this environment, it’s best to explicitly deactivate it. This ensures that the software installed for this workshop doesn’t interfere with your default Python setup or other projects.

Fallback option: cloud environment

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If a local installation does not work for you, it is also possible to run this lesson in Google colab. If you open a jupyter notebook there, the required packages are already pre-installed.