Fundamentals of Classification in Machine Learning

Classification is a core supervised learning technique used to categorise data into discrete classes. In this interactive 4-hour workshop, participants will explore key concepts in classification, including logistic regression, support vector machines (SVM), and neural networks. They will learn model evaluation techniques, such as precision-recall, ROC-AUC, and cross-validation. Through hands-on coding exercises in Python (Scikit-Learn, NumPy, Pandas, Matplotlib), attendees will build, interpret, and optimise classification models using real-world datasets. The workshop will also introduce strategies for handling imbalanced data and discuss model optimisation techniques. By the end of the session, participants will have practical skills to apply classification techniques to real-world predictive modelling tasks. No prior machine learning experience is required. However, basic Python and statistics knowledge are recommended.

This workshop is ideal for researchers, data scientists, and professionals looking to apply classification techniques to their datasets for predictive analysis.

By the end of this workshop, participants will be able to: 

• Define key concepts of classification in supervised learning, including different classification models and their applications. 

• Compare different classification techniques (logistic regression versus support vector machines versus neural networks) and explain when to use each. 

• Apply classification models to real-world datasets using Python. 

• Evaluate model performance using appropriate metrics, such as accuracy, precision-recall, F1-score, confusion matrix, and ROC-AUC. 

• Optimise classification models through feature engineering, hyperparameter tuning, and regularisation techniques.