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Course Outline
Module 1: Introduction to AI and Google Gemini
- Fundamentals of Artificial Intelligence (AI)
- Insights into Google Gemini AI and its broader ecosystem
- Distinguishing features and competitive advantages of Gemini compared to other AI models
- Practical Session: Investigating Gemini AI via the Google AI Studio demonstration
Module 2: Understanding Large Language Models (LLMs)
- Core principles behind large language models
- Internal architecture and operational mechanics of Gemini models
- Benchmarking Gemini against GPT and other industry-leading models
- Guided Lab: Visualizing tokenization processes and model outputs using sample prompts
Module 3: Getting Started with Gemini
- Configuring the necessary development environment
- Interacting with the Gemini API and SDK
- Managing authentication, access tokens, and API keys
- Initial Exercise: Executing your first Gemini prompt using Python
Module 4: Working with Gemini Models
- Examining the various Gemini model types and their specific capabilities
- Choosing the optimal models for linguistic, visual, or multimodal tasks
- Initializing and testing generative model performance
- Applied Task: Evaluating differences between text-to-text and image-to-text model outputs
Module 5: Practical Applications and Use Cases
- Embedding Gemini AI into chat interfaces and Q&A systems
- Crafting tools for semantic search and content summarization
- Navigating ethical AI usage and addressing bias considerations
- Group Project: Constructing a “Smart Research Assistant” leveraging NotebookLM and Gemini
Module 6: Advanced Features and Customization
- Refining prompt engineering strategies and managing complex contexts
- Leveraging Gemini for automated code generation and debugging
- Implementing fine-tuning workflows via Google Cloud Vertex AI
- Advanced Exercise: Modifying model behavior through parameters and temperature settings
Module 7: Real-World Projects and Collaboration
- Planning collaborative projects and establishing effective workflows
- Integrating Gemini AI with the broader Google suite (Drive, Docs, Sheets)
- Team Project: Designing and launching a compact AI application (such as a content summarizer, chatbot, or idea generator)
- Conducting peer reviews and discussing project outcomes
Module 8: Evaluation and Future Directions
- Resolving common challenges encountered in Gemini projects
- Reviewing the Gemini API roadmap and anticipated future features
- Adhering to best practices for AI governance and system scalability
- Final Reflection: Reviewing key practical lessons and their relevance to career development
Summary and Next Steps
Requirements
- A foundational grasp of core AI concepts
- Practical experience with API interactions and cloud-based services
- Competence in Python programming
Target Audience
- Software Developers
- Data Scientists
- Professionals and enthusiasts interested in AI
14 Hours
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