University of Queensland -
Applied Machine Learning
- Delivery: Face to Face
- Study Level: Other
- Duration: 2 days
Learn to apply machine learning for strategic decisions.
On this page
Course Overview
Harness the potential of machine learning (ML) to drive innovation and informed decision-making in your organisation. Specifically designed for non-technical professionals, this course simplifies complex ML concepts and equips you with the skills to evaluate and apply ML outcomes effectively.
From managing large datasets to working with text, audio or image data or collaborating with data experts, this program provides actionable insights to help you unlock ML’s value. Discover how to identify key opportunities, interpret results and leverage ML to gain a competitive edge in today’s data-driven world.
Key Facts
What you will study
- ML Fundamentals: Gain a solid understanding of key machine learning concepts and techniques, including working with multimodal data such as text, voice, audio, and images.
- Evaluating Outcomes: Learn to interpret machine learning results and assess their relevance to real-world business challenges.
- Strategic Integration: Discover how to identify opportunities for applying ML to address organisational problems and enhance decision-making processes.
- Data Management: Explore effective strategies for managing large-scale data and unlocking its value for strategic initiatives.
- Collaborative Communication: Develop the skills to confidently engage with technical teams and communicate ML insights effectively to stakeholders.
Who should attend
- Professionals who recognise the value of machine learning but are unsure how to apply it effectively.
- Decision-makers who need to understand and evaluate ML outcomes to make informed business choices.
- Individuals managing large-scale data who want to uncover its strategic value.
- Those who need to collaborate effectively with data and technical experts.
- Participants from data-driven industries, including non-technical consultants, healthcare, insurance, telecommunications, marketing or sectors working with text, voice or image data.
- No technical background is required. While a basic understanding of data is helpful, it is not essential.