This course consists of weekly 2-hour evening classes (6-8pm) over six weeks, with participants also needing to undertake approx. 2-3 hours weekly self-directed online learning activities.
The following content will be covered during this microcredential:
Improving machine learning results
- Imbalanced dataset
- Multiple data sources
- Feature engineering.
Advanced machine learning algorithms
- Polynomial linear regression
- Facebook prophet
- Gaussian mixture model
- Neural networks and deep learning with Pytorch.
Optimising machine learning models
- Model interpretation
- Automatic hyperparameter tuning.
Deploying machine learning solutions
- Experiment tracking
- Machine learning pipelines
- Machine learning versioning.
This microcredential is offered through a series of weekly online sessions facilitated by an industry expert. Each session will consist of a mix of subject presentations and hands-on experience. Participants will be able to learn the theory behind machine learning algorithms and data mining techniques followed by practical workshops, where they will apply what they've learnt to real-world business use cases.
In between sessions, participants will be required to engage in individual and collaborative online activities designed to support the understanding of the machine learning algorithms and their application.