This page attempts to detail my expertise in Artificial Intelligence (AI), by showcasing my related certificates and badges.
Ericsson Credly badges
New South Wales government microskills
These are endorsed by Australia's National AI Centre.
Microsoft badges
Multiple badges can be taken to complete a trophy. Completed trophies:
Skillsoft badges
A red-edged circular shield indicates an individual course that was successfully taken and passed. Multiple courses can be undertaken to complete a track: the orange-edged shield. Multiple tracks can be undertaken to complete an Aspire journey: the yellow-edged shield.
- Aspire journey
- individual track
- individual courses, where badges are issued.
- Prompt Engineering with Generative AI Tools
- Track 1: Prompt Engineering with Generative AI Tools
- Getting Started with Prompt Engineering
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- Exploring the OpenAI Playground
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- Prompt Engineering Techniques
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- Case Studies in Prompt Engineering
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- Final Exam: Prompt Engineering with Generative AI Tools
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- Generative AI Pitfalls
- Track 1: Generative AI Pitfalls
- Generative AI Pitfalls.
- An introduction to Generative AI from a related course: Generative AI Introduction and Overview.
- Leveraging Analytical and Critical Thinking to Implement AI from the course: Critical Thinking.
- Agentic AI mastery
- Track 1: Agentic AI - Foundations, Architectures, and Frameworks
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- Inside Agentic AI: Foundations and Frontiers.
- Inside Agentic AI: Core Architecture of Agentic Systems.
- Inside Agentic AI: Popular Frameworks.
- Agentic AI Design Patterns: Reusable Blueprints for Smarter Systems.
- Track 2: Agentic AI in Action
- Agentic AI in Action: Hands on with LangChain.
- Agentic AI in Action: Tool Use with LangChain.
- Agentic AI in Action: RAG with LangChain.
- Agentic AI in Action: Controlled Workflows with LangGraph.
- AI for Tech Leaders
- AI for Tech Leaders: Activate
- Fundamentals of AI and ML Literacy - benchmark
- Fundamentals of AI & ML: Foundational Data Science Models
- Fundamentals of AI & ML: Advanced Data Science Methods
- Fundamentals of AI & ML: Introduction to Artificial Intelligence
- Fundamentals of AI & ML: Metrics & Evaluation
- AI for Tech Leaders: Accelerate
- Emerging Data Trends Competency (Intermediate Level) - benchmark
- Emerging Data Trends: Navigating the Latest Trends in Data for Leaders
- Emerging Data Trends: Unveiling the Power of Practical Data Fabric
- Emerging Data Trends: Unlocking Data Observability
- Emerging Data Trends: Converged & Composable Systems
- Emerging Data Trends: AI TRiSM Unleashed
- AI for Tech Leaders: Transform
- AI and ML Data Strategy Competency (Intermediate Level) - benchmark
- Developing an AI/ML Strategy: The Data Analytics Maturity Model
- Developing an AI/ML Data Strategy: Building an AI-powered Workforce
- Developing an AI/ML Data Strategy: Data Analytics & Data Ethics
- Developing an AI/ML Data Strategy: Aspects of a Robust AI Strategy
- Developing an AI/ML Data Strategy: Data Bias & Ethical Considerations in AI
- Developing an AI/ML Data Strategy: Data Management & Governance in AI
- AI/ML GenAI for Decision-Makers and Leaders
- Track 1: AI & GenAI for Strategic Leaders
- Demystifying AI, ML and Generative AI for Leaders.
- The Strategic Value Proposition of AI/ML.
- Operationalizing AI Strategy: From Pilots to Scaled Enterprise Impact.
- Responsible AI Leadership: Ethics, Trust and Accountability.
- AI Regulation and Compliance: Strategy for Leaders.
- Data as a Strategic Asset: Foundations for AI/ML Success.
- Evaluating and Selecting AI/ML Solutions: A Strategic Leader's guide.
- Integrating AI/ML Into Existing Business Processes.
- Measuring the Business Impact of AI/ML Initiatives.
- Decision-Making and Critical Thinking in the Age of AI.
- Final Exam: AI & GenAI Leadership Strategy (added in update)
- Final Exam: Leading the AI-Powered Enterprise (added in update)
- Track 2: Managing and Deploying AI as Team Member
- AI Collaboration Protocols.
- AI Performance Management.
- Ethics, Trust and Accountability in AI Teams.
- Onboarding AI into Your Team.
- Scaling AI as a Team Member Across the Organization.
- Generative AI Business Transformation
- Foundations of Generative AI
- Generative AI and Its Impact to Everyday Business.
- Harnessing the Disruption of Generative AI.
- Responsible Application and Guardrails or Generative AI.
- Navigating AI Ethical Challenges and Risks.
- Recognizing Hallucinations, Inaccuracies, and Bias in AI.
- Establishing AI Guardrails and Governance.
- Reimagining Work with Generative AI
- Reimagining the Customer Experience with Generative AI.
- Reimagining the Sales Process with Generative AI.
- Unlocking Business Solutions with AI-Powered Analytics.
- Using AI to Improve the Employee Experience.
- Leading an AI Transformation
- Leading in the Age of Generative AI.
- Leading through the AI Disruption with Empathy (Global).
- Encouraging Innovation and Experimentation with AI.
- Human Skills to Sustain an AI Transformation
- Leveraging AI as a Team Member.
- Fostering a Growth Mindset in the Age of AI.
- Leveraging Analytical and Critical Thinking to Implement AI.
- Embracing Risk and Learning from Setback with AI Projects.
- AI for Frontline End-Users
- AI for Front-line End-users: Activate
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- AI in the Workplace.
- Responsible Use of AI.
- AI for Front-line End-users: Accelerate
- Discovering Copilot for Windows 11.
- Optimizing communication & information with Copilot for Microsoft 365.
- Creating documents efficiently with Copilot for Microsoft 365.
- Getting Started with Prompt Engineering.
- AI for Front-Line End-users: Transform
- Leveraging AI as a Team Member.
- Fostering a Growth Mindset in the Age of AI.
- LLMs in the Cloud
- Track 1: LLMs on Google Cloud
- LLMs on Google Cloud: Using Vertex AI Studio to Explore LLMs
- LLMs on Google Cloud: Text Generation Using Gemini Models
- LLMs on Google Cloud:Working with Batch Data, Images, Audio & Documents
- LLMs on Google Cloud: Embeddings, Function Calling, & Grounding with Gemini
- LLMs on Google Cloud: Retrieval-Augmented Generation (RAGs) on Vertex AI
- Final Exam: LLMs on Google Cloud
- Track 2: LLMs on Azure
- LLMs on Azure: Language Services on Azure AI
- LLMs on Azure: Language Features in Language Studio
- LLMs on Azure: Summarization with language Studio
- LLMs on Azure: Key Phrase Extraction & Entity Recognition
- LLMs on Azure: Detect & Redact PII
- LLMs on Azure: Extractive & Abstractive Document Summarization
- LLMs on Azure: Text Analytics for Health & Sentiment Analysis
- LLMs on Azure: Custom Text Classification
- LLMs on Azure: Extract Information with Azure AI Document Intelligence
- LLMs on Azure: Text Translation with Azure AI Translator
- LLMs on Azure: Azure AI Search Service
- LLMs on Azure: Retrieval Augmented Generation with OpenAI &AI Search Service
- Final Exam: LLMs on Azure
- LLM Metrics and Trade-Offs
- Track 1: LLM Metrics and Trade-Offs
- Large Language Models and Key Metrics
- LLM Accuracy, Performance, and Trade-Offs
- LLM Latency, Throughput, and Scalability
- LLM Cost Efficiency, Model Size, and Resource Optimization
- LLM Bias, Fairness, and Ethical Considerations
- Selecting the Right LLM
- Final Exam: LLM Metrics and Trade-Offs
- Introduction to Large Language Models (LLMs)
- Track 1: Introduction to Large Language Models (LLMs)
- Introduction to Large Language Models (LLMs)
- Agentic AI for Lawyers
- Track 1: Foundations of Agentic AI
- Track 2: Agentic AI in the Legal Domain
- AI for Finance Professionals