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Course Outline
Module 1: Introduction to AI and Google Gemini
- Defining Artificial Intelligence (AI)
- An overview of the Google Gemini AI ecosystem
- Distinctive 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)
- The core principles of large language models
- How Gemini models are structured and operate
- Comparing Gemini against GPT and other leading models
- Practice Lab: Visualizing tokenization processes and model responses using sample prompts
Module 3: Getting Started with Gemini
- Configuring the development environment
- Navigating the Gemini API and SDK
- Managing authentication, tokens, and API keys
- Hands-on Lab: Executing your first Gemini prompt using Python
Module 4: Working with Gemini Models
- Exploring 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
- Developing tools for semantic search and text summarization
- Considerations for ethical AI usage and bias mitigation
- Group Project: Creating a 'Smart Research Assistant' using NotebookLM and Gemini
Module 6: Advanced Features and Customization
- Optimizing prompts and managing complex contexts
- Applying Gemini for code generation and debugging
- Fine-tuning workflows using Google Cloud Vertex AI
- Hands-on Activity: Customizing model responses by adjusting parameters and temperature settings
Module 7: Real-World Projects and Collaboration
- Planning collaborative projects and establishing workflows
- Integrating Gemini AI with other Google tools (Drive, Docs, Sheets)
- Team Project: Designing and deploying 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 encountered in Gemini projects
- Exploring the Gemini API roadmap and upcoming features
- Best practices for AI governance and scalability
- Wrap-up Activity: Reflecting on practical lessons learned and their career implications
Summary and Next Steps
Requirements
- Familiarity with fundamental AI concepts
- Proficiency in using APIs and cloud-based services
- Experience with Python programming
Target Audience
- Software Developers
- Data Scientists
- AI Enthusiasts
14 Hours
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