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Course Outline
Module 1: Introduction to AI and Google Gemini
- Understanding Artificial Intelligence (AI)
- Survey of Google Gemini AI and its ecosystem
- Core features and competitive advantages of Gemini over other AI models
- Hands-on Activity: Exploring Gemini AI via the Google AI Studio demo
Module 2: Comprehending Large Language Models (LLMs)
- Foundational principles of large language models
- Architecture and functioning of Gemini models
- Comparing Gemini with GPT and other top-tier models
- Practice Lab: Visualizing tokenization and model outputs using sample prompts
Module 3: Kickstarting with Gemini
- Configuring the development environment
- Navigating the Gemini API and SDK
- Managing authentication, tokens, and API keys
- Hands-on Lab: Executing your initial Gemini prompt using Python
Module 4: Working with Gemini Models
- Exploring various Gemini model types and their capabilities
- Selecting the right models for language, image, or multimodal tasks
- Initializing and testing generative models
- Practical Exercise: Comparing outputs from text-to-text and image-to-text models
Module 5: Practical Applications and Use Cases
- Integrating Gemini AI into chat and Q&A systems
- Building semantic search and summarization tools
- Considerations for ethical AI usage and bias mitigation
- Group Project: Construct a “Smart Research Assistant” using NotebookLM and Gemini
Module 6: Advanced Features and Customization
- Optimizing prompts and handling advanced contexts
- Leveraging Gemini for code generation and debugging
- Implementing fine-tuning workflows with Google Cloud Vertex AI
- Hands-on Activity: Modifying model responses using parameters and temperature settings
Module 7: Real-World Projects and Collaboration
- Planning collaborative projects and setting up workflows
- Integrating Gemini AI with other Google tools (Drive, Docs, Sheets)
- Team Project: Design and deploy a compact AI application (e.g., content summarizer, chatbot, or idea generator)
- Peer review and discussion of project outcomes
Module 8: Evaluation and Future Directions
- Troubleshooting common issues in Gemini projects
- Exploring the Gemini API roadmap and emerging features
- Best practices for AI governance and scalability
- Wrap-up Activity: Reflection on practical lessons learned and potential career applications
Summary and Next Steps
Requirements
- Familiarity with fundamental AI concepts
- Practical experience with APIs and cloud services
- Proficiency in Python programming
Target Audience
- Software Developers
- Data Scientists
- AI Enthusiasts
14 Hours
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