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Course Outline
Module 1: Introduction to Artificial Intelligence and Google Gemini
- Definition and scope of Artificial Intelligence (AI)
- Strategic overview of the Google Gemini AI platform and its broader ecosystem
- Key capabilities and operational advantages of Gemini relative to competing AI models
- Practical Exercise: Initial exploration of Gemini AI via the Google AI Studio demonstration interface
Module 2: Fundamentals of Large Language Models (LLMs)
- Core principles underlying large language models
- Architectural design and operational mechanics of Gemini models
- Comparative analysis of Gemini against GPT and other industry-leading models
- Guided Lab: Analyzing tokenization processes and model outputs using sample prompts
Module 3: Establishing the Development Environment for Gemini
- Configuration of the required development environment
- Navigating the Gemini API and associated Software Development Kit (SDK)
- Managing authentication protocols, tokens, and API keys
- Practical Lab: Executing initial prompts with Gemini using Python
Module 4: Utilizing Gemini Models for Diverse Tasks
- Survey of distinct Gemini model types and their specialized capabilities
- Criteria for selecting appropriate models for text, image, or multimodal applications
- Initialization and validation procedures for generative models
- Operational Exercise: Evaluating output differences between text-to-text and image-to-text model configurations
Module 5: Operational Applications and Use Cases
- Incorporating Gemini AI into chat-based and query-and-answer systems
- Constructing semantic search engines and content summarization utilities
- Ethical standards for AI deployment and mitigation of bias
- Collaborative Project: Developing a “Smart Research Assistant” leveraging NotebookLM and Gemini for government-relevant workflows
Module 6: Advanced Features and Model Customization
- Prompt engineering techniques and advanced context management
- Leveraging Gemini for code generation and debugging support
- Fine-tuning procedures using Google Cloud Vertex AI
- Practical Activity: Adjusting model behavior through parameter configuration and temperature control
Module 7: Real-World Implementation and Team Collaboration
- Planning collaborative projects and establishing workflow standards
- Integrating Gemini AI with Google Workspace applications (Drive, Docs, Sheets)
- Team Project: Designing and deploying a functional AI application (e.g., content summarizer, interactive chatbot, or ideation tool) for government use cases
- Peer review and analysis of project outcomes
Module 8: Evaluation Metrics and Future Directions
- Troubleshooting common technical issues in Gemini-based projects
- Reviewing the Gemini API development roadmap and emerging features
- Adhering to best practices for AI governance, compliance, and scalability within public sector operations
- Closing Activity: Reflection on practical skills acquired and their application to career development in government technology
Summary and Next Steps
Requirements
- Familiarity with foundational artificial intelligence principles
- Proficiency in utilizing APIs and cloud infrastructure
- Competency in Python programming
Target Audience
- Software developers
- Data analysts and scientists
- Practitioners interested in AI applications for government
14 Hours
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