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Our introduction
The Right First Step
What can you expect
Your AI Learning Tutor - A quick guide
Introduction to AI: Why this section Matters
What is Intelligence?
What is Artificial Intelligence?
Types of AI and Capabilities
A Brief History of AI
AI in Action - Industry Applications
Section 1 - Knowledge Check
Section Summary and Key Takeaways
Understanding Machine Learning
Introduction to Machine Learning
Common Machine Learning Tasks
Learning Paradigms in Machine Learning
Common Machine Learning Algorithms
Model Training cycle
Model Evaluation (Optional)
Understanding Model Errors (Optional)
Section 2 - Knowledge Check
Section Summary and Key Takeaways
Section 2 - Reflection
AI in Business and Industry: Why This Section Matters
AI in the Real World
Theme 1: Predictive Decision-Making
Theme 2: Automation and Operational Efficiency
Theme 3: Personalised Engagement
Theme 4: Risk, Compliance, and Monitoring
Theme 5: Operational Optimisation and Resource Management
Theme 6: AI for Enhanced Customer and User Experience
Theme 7: AI for Innovation and New Product/Service Development
Section 3 - Knowledge Check
Section Summary and Key Takeaways
Section 3 - Reflection
Working With Data: Why This Section Matters
What is Data in AI
Structured vs. Unstructured Data
Features and Labels
Data Annotation
Data Sources and Collection
Data Quality and Cleaning
Ethical and Responsible Data Use
Section 4 - Knowledge Check
Section Summary and Key Takeaways
Section 4 - Reflection
AI Tools and Platforms: Why This Section Matters
AI Tools in Business
Types of AI Tools
Cloud-Based AI Platforms
Choosing the Right AI Tool
Hands On Activity 1 - Predicting Customer Churn
Hands on Activity 2 - Image Classification
Section 5 - Knowledge Check
Section Summary and Key Takeaways
What Is Generative AI?
How does GenAI work
Business Applications of Generative AI
Working with AI Assistants
Introduction to Prompting
Hands on Prompting
Keeping up with GenAI
Section 6 - Knowledge Check
Section Summary and Key Takeaway
AI Lifecycle: Why this section matters
AI Lifecycle: Why this section matters
The AI Project Lifecycle
1: Inception - Framing the Problem
2: Feasibility and Risk Assessment
3: Designing the AI Solution
4: Building and Testing the AI Model
5: Deployment and Integration
6: Monitoring and Continuous Improvement
7: Review and Re-Evaluation
Section 7 - Knowledge Check
Section Summary and Key Takeaway
Safe, Ethical and Responsible AI: Why This Section Matters
Responsible AI — Foundations
Common AI Risks and Limitations
Bias and Fairness in AI
Transparency and Explainability
AI Governance
Australia’s AI Ethics Principles
Voluntary AI Safety Standard
Section 8 - Knowledge Check
Section Summary and Key Takeaway
The Future Is Not AI. It’s Human–AI.
Reflections