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Course Outline
Foundations of AI Programming
- Defining AI programming: Key concepts and real-world examples.
- Public sector applications of AI: Chatbots, summarizers, and intelligent search systems.
- Comparing AI models with traditional programming logic.
Introductory Python for AI
- Writing your first Python scripts.
- Working with data structures and control logic.
- Essential libraries for AI programming: requests, pandas, and json.
Using AI APIs
- Understanding APIs and securely accessing AI models.
- Sending text and structured data to AI models.
- Working with APIs from OpenAI, Cohere, or Hugging Face.
Creating Simple AI Tools
- Building a document summarizer.
- Prototyping a chatbot for citizen services.
- Using AI to automatically label public datasets.
Evaluating Outputs and Limitations
- Understanding the probabilistic nature of AI behavior.
- Prompt engineering techniques and managing output quality.
- Testing prototypes for bias and hallucinations.
Compliance, Ethics, and Responsible Development
- Privacy and explainability requirements in government contexts.
- Open-source vs. proprietary models: Advantages and disadvantages.
- Checklist for safe experimentation and scaling up AI projects.
Summary and Next Steps
Requirements
- Basic experience working with spreadsheets or structured data.
- Familiarity with public sector service delivery or analytical tasks.
- No prior programming experience is required, as introductory Python concepts will be covered.
Audience
- Public servants and analysts looking to explore AI integration into their daily workflows.
- Digital government professionals seeking practical skills in AI implementation.
- Innovation, transformation, and research teams within government agencies.
14 Hours
Testimonials (1)
I got to learn more about Microsoft Copilot, something that I thought was the same as chatGPT but I got to discover more exciting options that I will forever use to make my life easy.
