Online
26-29 March 2024
10 am - 1 pm
Instructors: Ben King
Helpers: Hywel Dunn-Davies, Alan O'Callaghan
Introduction to Machine Learning with Python
This workshop comprises four lessons on applied machine learning in Python using health data. Lessons take participants through a typical pipeline for prediction, covering key concepts in preparing data, training models, and evaluating performance. We introduce models including decision trees and neural networks and highlight key issues in their responsible use. Prior knowledge of Python (for example, gained through a Carpentries course) is beneficial, but not required.
Ed-DaSH
Ed-DaSH is a Data Science training programme for Health and Biosciences. The team has developed workshops using The Carpentries platform on the following topics. See workshops for dates and registration details. All workshops will be delivered remotely.
Where: This training will take place online. The instructors will provide you with the information you will need to connect to this meeting.
When: 26-29 March 2024. Add to your Google Calendar.
Requirements: Participants must have access to a computer with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed below).
Accessibility: We are dedicated to providing a positive and accessible learning environment for all. Please notify the instructors in advance of the workshop if you require any accommodations or if there is anything we can do to make this workshop more accessible to you.
Contact: Please email andrzej.romaniuk@ed.ac.uk for more information.
Roles: To learn more about the roles at the workshop (who will be doing what), refer to our Workshop FAQ.
Everyone who participates in Carpentries activities is required to conform to the Code of Conduct. This document also outlines how to report an incident if needed.
We will use this collaborative document for chatting, taking notes, and sharing URLs and bits of code.
Please be sure to complete the workshop survey (can be completed before you attend).
The lesson taught in this workshop is being piloted and a precise schedule is yet to be established.
| Lessons | |
| Introduction to machine learning | Introduction |
| Data preparation | |
| Learning | |
| Modelling | |
| Validation | |
| Evaluation | |
| Bootstrapping | |
| Data leakage | |
| Lessons | |
| Tree models | Introduction |
| Decision trees | |
| Variance | |
| Boosting | |
| Bagging | |
| Random forest | |
| Gradient boosting | |
| Evaluation | |
| Lessons | |
| Neural networks | Introduction |
| Visualisation | |
| Data preparation | |
| Neural networks | |
| Training and evaluation | |
| Explainability | |
| Lessons | |
| Responsible machine learning | Introduction |
| Tasks | |
| Data | |
| Fairness | |
| Dataset shift | |
| Explainability | |
| Attacks | |
To participate in a workshop, you will need access to software as described below. In addition, you will need an up-to-date web browser.
We maintain a list of common issues that occur during installation as a reference for instructors that may be useful on the Configuration Problems and Solutions wiki page.
If you haven't used Zoom before, go to the official website to download and install the Zoom client for your computer.
Like other Carpentries workshops, you will be learning by "coding along" with the Instructors. To do this, you will need to have both the window for the tool you will be learning about (a terminal, RStudio, your web browser, etc..) and the window for the Zoom video conference client open. In order to see both at once, we recommend using one of the following set up options:
Please check the “Setup” page of the lesson site for instructions to follow to obtain the software and data you will need to follow the lesson.