Introduction to Machine Learning with Scikit Learn

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

Schedule

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

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