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Course Outline
Foundations of AI Programming
- Defining AI programming: Key concepts and illustrative examples
- Public sector AI applications: chatbots, summarizers, and intelligent search
- Comparison of AI models and traditional programming logic
Introductory Python for AI
- Writing initial Python scripts
- Managing data structures and control logic
- Essential libraries for AI programming: requests, pandas, json
Utilizing AI APIs
- Understanding APIs: Secure access to AI models
- Transmitting text and structured data to models
- Working with APIs from OpenAI, Cohere, or Hugging Face
Developing Simple AI Tools
- Constructing a document summarizer
- Prototyping a chatbot for citizen services
- Employing AI to automatically label public datasets
Evaluating Outputs and Limitations
- Understanding the probabilistic nature of AI behavior
- Prompt engineering and managing output quality
- Red-teaming prototypes to identify bias and hallucinations
Compliance, Ethics, and Responsible Development
- Privacy and explainability requirements in government settings
- Open-source versus proprietary models: advantages and disadvantages
- Checklist for safe experimentation and scaling up
Summary and Next Steps
Requirements
- Fundamental experience working with spreadsheets or structured data
- Familiarity with public sector service delivery or analytical tasks
- No prior programming experience is necessary (introductory Python will be included)
Target Audience
- Public servants and analysts exploring AI integration in daily workflows
- Digital government professionals seeking practical skills in AI implementation
- Innovation, transformation, and research teams within the government
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.