Clustering and Unsupervised Methods in Machine Learning (Nov 2026)

Unsupervised learning techniques, particularly clustering, are essential for discovering hidden patterns and structures in data. In this interactive workshop, participants will explore key unsupervised learning methods, including k-means clustering, hierarchical clustering, and density-based clustering. The session will also introduce dimensionality reduction techniques, including principal component analysis (PCA) and t-distributed stochastic neighbour embedding ( t-SNE ) for visualisation and feature extraction. Through interactive coding exercises in Python (Scikit-Learn, NumPy, Pandas, Matplotlib, Seaborn), attendees will apply clustering techniques to real-world datasets and learn how to evaluate the quality of clustering results. The workshop will also discuss applications of clustering in different domains, from customer segmentation to anomaly detection. By the end of the session, participants will have the practical skills to apply clustering and unsupervised learning methods effectively in data analysis. No prior machine learning experience is required, but basic Python and statistics knowledge are recommended.

FAQs

What should I bring to 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.

What prior experience do I need?

• Basic Python knowledge.

• Fundamental statistics knowledge.

Who should attend?

This workshop is ideal for researchers, data analysts, and professionals looking to apply clustering techniques to explore and structure large datasets without labelled outputs.
How can I contact the organiser with any questions?

You can email training@qcif.edu.au.

What's the cancellation policy?

Cancellations are accepted up to 3 working days before 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.