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Take the next step in learning how to design and implement innovative solutions to complex problems using state-of-the-art machine learning algorithms and data science approaches.
Co-designed by renowned academics and industry partners from the UTS Master of Data Science and Innovation program and delivered one of the industry's leading experts, Anthony So, this interactive course will allow you to leverage the latest data science approaches and best practices to transform your approach to developing data-driven insights and solutions within any organisation.
In this microcredential, you will learn advanced machine learning concepts and techniques in depth, such as machine learning pipeline, versioning, gradient boosting and neural networks. You will also gain skills that help you better manage production-ready end-to-end solutions.
Featuring a uniquely transdisciplinary approach to learning, this course will give you advanced skills in tackling complex problems, providing transferrable skills across a broad range of industries, sectors and organisations.
This dynamic, innovative approach, combined with hands-on learning and practice will help you become well versed in implementing, optimising and maintaining advanced machine learning solutions that can disrupt industries or change people’s lives for the better.
This microcredential aligns with the 4 credit point subject, Advanced Data Science for Innovation (36114) in the Master of Data Science and Innovation (C04372).
This microcredential may qualify for recognition of prior learning at this and other institutions.
This microcredential is suitable for anyone interested in learning more about machine learning, such as:
This advanced course focuses on best practices, pipeline, automation, advanced algorithms so that students are prepared for challenging and complicated ML problems. It guided me through thinking deeply into why and when to use all available tools and algorithms, and how to explain and convince stakeholders that the decision is completely data driven.
I highly recommend this course to people who have a passion for ML or want to do ML in the right way.
Full price: $2,648.00 (GST free)*
*Price subject to change. Please check price at time of purchase.
Discounts are available for this course. For further details and to verify if you qualify, please check the Discounts section under Additional course information.
COVID-19 response
This course consists of weekly 3-hour evening classes (Wednesdays, 5.30pm-8.30pm) 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
Advanced machine learning algorithms
Optimising machine learning models
Deploying machine learning solutions
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.
By the end of the microcredential you will be able to:
Assessment task one - Machine learning project
Assessment task two - Kaggle competition
Participants must achieve at least 50% of the course’s total marks and complete all assessments.
Mandatory
Desired
Discounts are available for this course as follows:
Discounts cannot be combined and only one discount can be applied per person per course session. Discounts can only be applied to the full price. Discounts cannot be applied to any offered special price.
How to enrol and obtain your UTS staff discount (UTS staff)
How to apply your discount voucher
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START DATE |
22 September |
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MODE |
Online |
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DURATION |
6 wks |
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COMMITMENT |
Avg 14 hrs p/w |
Anthony is a renowned leader in data science. He has extensive experience in solving complex business problems using advanced analytics and AI in different industries, including financial services, media and telecommunications. He is currently the Head of Data Science of one of the fastest-growing startups in the APAC region.
He is also the author of several best-selling books on data science, machine learning and deep learning. He has also won multiple prizes at several hackathon competitions such as Unearthed, GovHack and Pepper Money.
Anthony holds two Master’s Degrees in Computer Science and Data Science and Innovation.
Tony is the course director of Master of Data Science and Innovation at UTS TD School. He is a data visualisation researcher with expertise in visual analytics and human computer interaction and designs visualisations, user interfaces and interaction methods to combine data values with human intelligence for effective data exploration, communication and decision making. He has over 150 publications, including five books.
Tony is also interested in teaching inspired research and research inspired teaching, actively exploring and practicing innovative teaching methods for optimal student learning experience and performance. He is experienced in course curriculum design and subject development. Both of Tony's teaching and research projects have been supported by the Australian Research Council and Australian Government Office for Teaching and Learning, including prestigious and competitive ARC DP and LP projects. He gives keynote/plenary speeches, invited talks and seminars at conferences and universities worldwide.
Enrolments close Wednesday, 15 September 2021.
Take the next step in solving complex business problems with innovative solutions using advanced machine learning and data science tools. [6 wks, avg 14 hrs/wk]
Learn how to solve challenging business problems and drive innovation using machine learning and related data science tools. [6 wks, avg 14 hrs/wk]
