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Course Outline
Module 1: Overview of AI and Google Gemini
- Defining Artificial Intelligence (AI)
- Exploring the Google Gemini AI ecosystem
- Distinct features and benefits of Gemini compared to other AI models
- Practical Activity: Discovering Gemini AI via the Google AI Studio demonstration
Module 2: Insights into Large Language Models (LLMs)
- Core principles of large language models
- Architecture and operational mechanics of Gemini models
- Benchmarking Gemini against GPT and other leading models
- Exercise Lab: Visualizing tokenization and model responses with sample prompts
Module 3: Initiating Work with Gemini
- Establishing the development environment
- Engaging with the Gemini API and SDK
- Managing authentication, tokens, and API keys
- Interactive Lab: Executing your initial Gemini prompt using Python
Module 4: Utilizing Gemini Models
- Examining various Gemini model types and their capabilities
- Choosing suitable models for language, image, or multimodal tasks
- Initializing and testing generative models
- Applied Exercise: Evaluating outputs from text-to-text and image-to-text models
Module 5: Practical Applications and Scenarios
- Incorporating Gemini AI into chat and Q&A systems
- Creating semantic search and summarization utilities
- Addressing ethical AI usage and bias concerns
- Team Exercise: Constructing a “Smart Research Assistant” leveraging NotebookLM and Gemini
Module 6: Advanced Capabilities and Customization
- Refining prompts and managing advanced context
- Employing Gemini for code generation and debugging
- Implementing fine-tuning workflows via Google Cloud Vertex AI
- Interactive Activity: Tailoring model responses through parameters and temperature settings
Module 7: Real-World Projects and Teamwork
- Planning collaborative projects and setting up workflows
- Connecting Gemini AI with other Google platforms (Drive, Docs, Sheets)
- Group Exercise: Designing and deploying a compact AI application (e.g., content summarizer, chatbot, or idea generator)
- Conducting peer reviews and discussing project outcomes
Module 8: Assessment and Future Trends
- Resolving common challenges in Gemini projects
- Investigating the Gemini API roadmap and forthcoming features
- Adopting best practices for AI governance and scalability
- Closing Activity: Reflecting on key lessons and their career implications
Conclusion and Recommended Next Steps
Requirements
- A foundational understanding of basic AI concepts
- Familiarity with APIs and cloud services
- Programming experience in Python
Target Audience
- Software Developers
- Data Scientists
- AI Enthusiasts
14 Hours
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