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.

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Prerequisite

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. Either of the Software Carpentry Python courses cover sufficient background.

	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.

Requirements

Software

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

Callout

Installing Python

[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

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

Setup

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

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

To create a new virtual environment (“venv”) called “intro_ml” for the project, open the terminal (Max/Linux), Git Bash (Windows) or Anacomda 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 {: .info}

Activate environment

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

Installing your prerequisites

Install the prerequisites:

BASH

pip install numpy pandas matplotlib opencv-python scikit-learn

Deactivating/activating environment

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

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.