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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.
This microcredential will equip you to:
This microcredential may qualify for recognition of prior learning at this and other institutions.
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: $1,595.00 (GST-free)*
*Price subject to change. Please check price at time of purchase.
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:
- scoping the business problem
- defining success criteria
- understanding your organisation's data.
- Data collection and pre-processing
- Model building and pattern discovery
- Model validation and deployment
- Model monitoring, decay and adaptation.
- Reports, presentations, prototypes, dashboards.
- Anticipating the barriers to achieving benefits and how to work with champions to realise success
- Limitations of machine learning and how to communicate them.
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
Length: 2,000 – 2,500 words.
|Avg 5 hrs p/w|
Jun Li is a senior lecturer at UTS with extensive experience in teaching machine learning and data analytics. He conducts research in machine learning theory and applications, including probabilistic data models, neural network learning and image and video analytics.
Jun received his PhD in computer science from Queen Mary, University of London, UK.
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