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Applied Machine Learning



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10 wks


Avg 5 hrs/wk

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Applied Machine Learning develops the autonomy of participants to plan and implement a machine learning (ML) project using the whole life cycle, from problem understanding to deployment of results. This practical, problem-based microcredential enables demonstration of machine learning expertise and professional communication skills.

About this microcredential

This microcredential will allow you to develop your skills to apply the full machine learning project lifecycle to professional projects and business challenges. You'll work through a series of case studies and challenges with our expert facilitators and build up a skillset to implement machine learning solutions to specified business problems. You’ll learn to explain and justify your approach, design and implement a range of ML models and clearly communicate the outcomes and insights.

Key benefits of this microcredential

This microcredential will equip you to:

  • Build the autonomy to plan and implement a machine learning project all the way from problem definition through to solution development, deployment and communication of outcomes
  • Appreciate a highly practical, business-focused program which incorporates demonstrating an understanding of key ML concepts through application to case studies.

This microcredential aligns with the 2-credit point subject, Applied Machine Learning (42892) in the Master of Information Technology (C04295).

This microcredential may qualify for recognition of prior learning at this and other institutions.

Who should do this microcredential?

This microcredential is designed for professionals with hands-on experience in machine learning, who are looking to enhance and demonstrate their expertise in the professional design, delivery and communication of machine learning projects.


Full price: $1,595 (GST-free)*

*Price subject to change. Please check price at time of purchase. 

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.

Additional course information

Course outline

During this microcredential you will meet and work with a dedicated course facilitator, who will support your learning and engagement with the teaching resources designed by the lead academic and a team of experts in the Faculty of Engineering and IT.

The microcredential comprises online modules each featuring self-study materials and facilitated live sessions. Throughout this course you will review and expand on key concepts in machine learning through case studies and practical problems, including:

  • Understanding where machine learning fits into business
  • Who are the stakeholders of machine learning and what their perspectives, skills and needs involve
  • How to contribute high value to the business with machine learning

- scoping the business problem

- defining success criteria

- understanding your organisation's data.

  • Machine Learning solution development

- Data collection and pre-processing

- Model building and pattern discovery

- Model validation and deployment

- Model monitoring, decay and adaptation.

  • Insight generation – machine learning outputs and realising the benefits

- Reports, presentations, prototypes, dashboards.

  • Communication of the outcomes of the models to business stakeholders

- Anticipating the barriers to achieving benefits and how to work with champions to realise success

- Limitations of machine learning and how to communicate them.

Course learning objectives

Upon successful completion of this microcredential, participants will be able to implement a machine learning project in a business environment.


Machine learning project: Proof of concept

  • The task involves developing a machine learning project using a given case study. The project is self-directed, with support of the academic and is an iterative process covering business understanding, data understanding, data preparation, modelling, evaluation and deployment.
  • A written report describing the business problem, stakeholders, approaches deployed and results and reflection of the in-class oral feedback on the approach and progress is required to be submitted.

Length: 2,000 – 2,500 words.



  • To complete this online course, you will need a personal computer with adequate internet access and sufficient software and bandwidth to support web conferencing. You will also require an operating system with a web browser compatible with Canvas, Zoom and Microsoft Teams



  • Demonstrated knowledge and experience using key machine learning models.

Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.