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Course Outline
Basics of AI Coding
- Definition of AI coding: main ideas and real-world examples
- AI uses in the public sector: chatbots, summarizing tools, smart search
- AI models compared to standard coding logic
Beginner Python for AI
- Creating your first Python scripts
- Handling data structures and logic controls
- Useful AI coding libraries: requests, pandas, json
Working with AI APIs
- Understanding APIs: accessing AI models safely
- Passing text and structured data to models
- Using OpenAI, Cohere, or Hugging Face APIs
Building Simple AI Tools
- Developing a document summarizer
- Creating a chatbot prototype for citizen services
- Using AI to automatically tag public datasets
Assessing Results and Limits
- Grasping the unpredictable nature of AI
- Prompt engineering and controlling output quality
- Testing prototypes for bias and hallucinations
Compliance, Ethics, and Safe Development
- Privacy and transparency needs in government
- Open-source vs. proprietary models: advantages and disadvantages
- Checklist for safe testing and expansion
Recap and Future Steps
Requirements
- Basic knowledge of using spreadsheets or handling structured data
- Some familiarity with public sector service delivery or analytical tasks
- No previous coding experience is needed (beginner Python will be taught)
Intended Participants
- Public sector employees and analysts looking to use AI in their everyday work
- Digital government specialists aiming to gain practical 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.