Introduction to Machine Learning with Scikit Learn

This workshop will be an intro to a variety of common machine learning techniques using SciKit Learn. Please bring along a laptop!

There’s more material than we have time for, so we’ll adjust pace based on how everyone is doing. There’ll be helpers available if people have questions from working through at their own pace.

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 software requirements as described in the setup page

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	Break
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 can we perform unsupervised learning with dimensionality reduction techniques such as Principle Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE)?
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.