New Machine Learning using Python courses added to Intersect course catalogue

Machine Learning is the process of teaching a computer system how to make accurate predictions when fed with data enabling computers to tackle enormous tasks and come up with predictions to complex problems. In other words, Machine Learning uses statistics to find patterns in massive amounts of data, and therefore, it has been widely used by researchers from any discipline to find answers to their research problems.

Intersect developed a comprehensive introduction to Machine Learning basics that covers all the terminology and concepts used in Machine Learning, as well as the intuition behind certain Machine Learning models and techniques. This introductory series of Machine Learning consists of three courses where we demonstrate the application of Machine Learning models and techniques using Python and related libraries on real datasets. The topics covered in these courses include Linear Regression, Model Training, Logistic Regression, Decision Tree, Ensemble Learning, Support Vector Machine, K-Nearest Neighbour, and Dimensionality Reduction.

In November and December 2020, Intersect delivered this series of Machine Learning workshops for the first time. A total number of 78 researchers from Intersect member universities attended three courses. Khuong Tran, eResearch Trainer and a PhD candidate at UTS, who developed the course material, led all three courses, with assistance from Ghulam Murtaza, eResearch Services Manager, Jerry Lai, eResearch Analyst from Deakin University, and Anastasios Papaioannou, eResearch Training Manager & Lead Research Data Scientist. Initial feedback from participants was excellent, with a Net Promoter Score of +76 and average scores on the five other primary metrics for quality of training exceeding 9.1 out of 10.