This workshop will take place on Wednesday, 26th August 9:30-1:30pm AEST.

Regression is a fundamental technique in supervised machine learning which is used to predict continuous outcomes based on input data. In this interactive 4-hour workshop, participants will explore the core concepts of regression, including simple and multiple linear regression, regularisation techniques (Ridge & Lasso), model evaluation and touch on Bayesian methods and uncertainty quantification. Through hands-on coding exercises in Python (Scikit-Learn, statsmodels, cmdstanpy, NumPy, Pandas, Matplotlib), attendees will learn how to build, interpret, and optimise regression models using real-world datasets. By the end of the session, participants will have the practical skills to apply regression techniques to solve predictive modelling problems effectively. No prior machine learning experience is required, however basic Python and statistics knowledge is required.

FAQs

What should I bring into the event?

This is an online workshop, so you will need to provide your own computer, and have access to a reliable internet connection sufficient for video conferencing. A second monitor is highly recommended. This workshop will run on GoogleColab, setup instructions will be provided prior to the workshop.

What prior experience do I need?

- Basic Python knowledge (for example, having completed Software Carpentry ‘Plotting and Programming in Python’)

- Fundamental statistics knowledge

Who should attend?

This workshop is ideal for researchers looking to apply machine learning techniques to their data, enabling them to build and evaluate regression models for predictive analysis.

How can I contact the organiser with any questions?

You can email training@qcif.edu.au.

What's the cancellation policy?

Cancellations may be made up to 3 working days prior to the start of the workshop. If you do not join on the day without having cancelled beforehand, you may be blocked from attending any future QCIF training workshops.

Contact the host of Regression in Machine Learning (Aug 2026)

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