By the end of the course you should be able to:
- Explain to a peer how machine learning algorithms can be applied and the background behind these algorithms
- Program in Python to solve common machine learning problems
- Apply machine learning related Python tools, including numpy and scikit-learn libraries.
Digital badge and certificate
A digital badge and certificate will be awarded following the successful completion of any necessary tasks or assessments to demonstrate acquired learning of the short course or for meeting attendance and/or participation requirements.
Learn more about UTS Open digital badges.
Course outline
The following content will be covered during this course:
- Overview of modern-day data analytics and machine learning
- Machine learning fundamentals
- Introduction to Python and basic operations
- Cluster analysis
- Performing clustering algorithms
- Classification and regression
- Programming regression models and logistic regression
- Time series modelling
- Inventory modelling with autoregressive integrated moving average and Hidden Markov Model
- Recommendation systems
- Collaborative filtering and non-negative matrix factorisation
- Conclusion, action planning and next steps.
Enrolment conditions
Course purchase is subject to UTS Open Terms and Conditions.
COVID-19 response
UTS complies with latest Government health advice. Delivery of all courses complies with the UTS response to COVID-19.
Contact us
For any questions on enrolment or payment, please email support@open.uts.edu.au
If you have a specific question on course content or requirements, please feitshortcourses@uts.edu.au
