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.
Introduction to Python programming
- Installation and setup
- Data structures
- Conditions and loops.
Running machine learning projects
- Different types of learning
- Machine learning approaches for tackling business problems
- Machine learning and Agile methodology.
Exploring data
- Descriptive statistics
- Visualising data
- Identifying and fixing issues
- Feature engineering.
Machine learning algorithms
- Univariate and multivariate linear regression
- Logistics regression
- K-Nearest Neighbors
- Decision tree
- Random forest
- K-means.
Optimising machine learning
- Model evaluation
- Assessing underfitting and overfitting
- Hyperparameter tuning
- Model interpretation.
Deploying machine learning solutions
- Lifecycle of machine learning models
- Machine learning pipelines and artefacts
- Machine learning as a service.
Course delivery
This course offers in a series of weekly, interactive online sessions facilitated by a leading industry expert. Each session consists 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.
