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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
- Key features and competitive advantages of Gemini compared to other AI models
- Practical Exercise: Exploring Gemini AI via the Google AI Studio demo
Module 2: Understanding Large Language Models (LLMs)
- Core principles of large language models
- The architecture and operational mechanics of Gemini models
- Comparing Gemini against GPT and other leading models
- Lab Work: Visualizing tokenization and model responses using sample prompts
Module 3: Initial Setup with Gemini
- Configuring the development environment
- Working with the Gemini API and SDK
- Managing authentication, tokens, and API keys
- Hands-on Lab: Executing your first Gemini prompt using Python
Module 4: Utilizing 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 Task: 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 applications
- Developing semantic search and summarization tools
- Considerations for ethical AI usage and bias
- Group Project: Building 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
- Practical Exercise: Customizing model responses through parameter and temperature control
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: Designing and deploying a small-scale AI application (e.g., content summarizer, chatbot, or idea generator)
- Conducting peer reviews and discussing project outcomes
Module 8: Evaluation and Future Directions
- Troubleshooting common issues in Gemini projects
- Exploring the Gemini API roadmap and upcoming features
- Best practices for AI governance and scalability
- Closing Activity: Reflecting on practical lessons learned and their application to careers
Summary and Next Steps
Requirements
- A foundational understanding of basic AI concepts.
- Experience working with APIs and cloud services.
- Proficiency in Python programming.
Intended Audience
- Developers
- Data scientists
- AI enthusiasts
14 Hours
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