Course overview
The aim of this course is to enable students to perform data exploration, visualisation, and analysis using predictive modelling techniques. The aim of this course is topics covered in this course include: classification: naive bayes, decision trees, SVM, Neural Networks, random forest, ensemble; regression: linear, logistic; model evaluation, overfitting, pruning.
Course learning outcomes
- Develop accurate predictive models based on large data sets.
- Perform predictive analytics on large data sets using an industry standard software tool-set.
- Communicate appropriately with professional colleagues through visualisation and report.
- Select and implement supervised machine learning techniques that are appropriate for the applications under consideration.
- Select suitable model parameters for different supervised machine learning techniques.
Degree list
The following degrees include this course