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Course Outline
Module 1: Introduction to AI and Google Gemini
- Understanding Artificial Intelligence (AI).
- Overview of the Google Gemini AI ecosystem.
- Key features and advantages of Gemini compared to other AI models.
- Hands-on Activity: Exploring Gemini AI via the Google AI Studio demo.
Module 2: Understanding Large Language Models (LLMs)
- Fundamentals of large language models.
- Architecture and operational mechanics of Gemini models.
- Comparison of Gemini with GPT and other top-tier models.
- Practice Lab: Visualizing tokenization and model responses using sample prompts.
Module 3: Getting Started with Gemini
- Configuring the development environment.
- Utilizing the Gemini API and SDK.
- Managing authentication, tokens, and API keys.
- Hands-on Lab: Executing your first Gemini prompt with Python.
Module 4: Working with Gemini Models
- Exploring various Gemini model types and their capabilities.
- Selecting suitable 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 applications.
- Developing tools for semantic search and content summarization.
- Understanding ethical AI usage and addressing bias.
- Group Project: Building a “Smart Research Assistant” using NotebookLM and Gemini.
Module 6: Advanced Features and Customization
- Optimizing prompts and managing complex context.
- Leveraging Gemini for code generation and debugging.
- Fine-tuning workflows through Google Cloud Vertex AI.
- Hands-on Activity: Customizing model responses using parameters 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).
- 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 upcoming features.
- Best practices for AI governance and scalability.
- Wrap-up Activity: Reflecting on practical lessons learned and career applications.
Summary and Next Steps
Requirements
- Familiarity with fundamental AI concepts.
- Experience working with APIs and cloud services.
- Proficiency in Python programming.
Audience
- Software Developers
- Data Scientists
- AI Enthusiasts
14 Hours
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