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Course Outline
Module 1: Introduction to AI and Google Gemini
- Defining Artificial Intelligence (AI)
- Insight into Google Gemini AI and its broader ecosystem
- Core features and benefits of Gemini compared to other AI models
- Hands-on Activity: Exploring Gemini AI via the Google AI Studio demonstration
Module 2: Understanding Large Language Models (LLMs)
- Core principles of large language models
- How Gemini models are architected and operate
- Evaluating Gemini against GPT and other top-tier models
- Practice Lab: Visualizing tokenization processes and model outputs using sample prompts
Module 3: Getting Started with Gemini
- Preparing 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
- Investigating various Gemini model types and their capabilities
- Choosing the right models for language, image, or multimodal tasks
- Initializing and testing generative models
- Practical Exercise: Comparing outputs from text-to-text versus image-to-text models
Module 5: Practical Applications and Use Cases
- Integrating Gemini AI into chatbots and Q&A systems
- Building tools for semantic search and content summarization
- Considerations for ethical AI usage and bias mitigation
- Group Project: Constructing a “Smart Research Assistant” utilizing NotebookLM and Gemini
Module 6: Advanced Features and Customization
- Optimizing prompts and managing complex context
- Employing Gemini for code generation and debugging
- Refining workflows via Google Cloud Vertex AI
- Hands-on Activity: Tailoring model responses through parameter adjustments and temperature control
Module 7: Real-World Projects and Collaboration
- Planning collaborative projects and establishing workflows
- Integrating Gemini AI with other Google ecosystem tools (Drive, Docs, Sheets)
- Team Project: Designing and deploying a compact AI application (e.g., content summarizer, chatbot, or idea generator)
- Peer evaluation and discussion of project outcomes
Module 8: Evaluation and Future Directions
- Troubleshooting frequent challenges in Gemini projects
- Reviewing the Gemini API roadmap and anticipated features
- Adhering to best practices for AI governance and scalability
- Wrap-up Activity: Reflecting on practical insights gained 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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