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Course Outline
Foundations of AI Programming
- Defining AI programming: key concepts and real-world examples
- AI applications in the public sector: chatbots, summarization tools, and intelligent search
- Comparing AI models with traditional programming logic
Introductory Python for AI
- Developing your first Python scripts
- Managing data structures and control flow logic
- Essential libraries for AI programming: requests, pandas, and json
Leveraging AI APIs
- Understanding APIs: secure access to AI models
- Transmitting text and structured data to models
- Utilizing APIs from providers such as OpenAI, Cohere, or Hugging Face
Building Simple AI Tools
- Creating a document summarization tool
- Prototyping a citizen service chatbot
- Applying AI for automated labeling of public datasets
Assessing Outputs and Constraints
- Understanding the probabilistic nature of AI behavior
- Prompt engineering techniques for managing output quality
- Conducting red-team exercises to identify bias and hallucinations in prototypes
Compliance, Ethics, and Responsible Development
- Privacy and explainability mandates in government contexts
- Advantages and disadvantages of open-source versus proprietary models
- Checklists for safe experimentation and scaling solutions
Recap and Next Steps
Requirements
- Foundational experience in working with spreadsheets or structured data
- Familiarity with public sector service delivery or analytical tasks
- No prior programming background is necessary, as introductory Python concepts will be covered
Target Audience
- Public servants and analysts looking to integrate AI into their daily operations
- Digital government professionals aiming to acquire hands-on AI integration skills
- Government teams focused on innovation, transformation, and research
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.