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Duration 14 hours
Course Outline
Module 1: Introductory Concepts of AI and Google Gemini
- Defining Artificial Intelligence (AI)
- An overview of the Google Gemini AI ecosystem
- Distinct features and benefits of Gemini compared to other AI models
- Practical Exercise: Exploring Gemini AI via the Google AI Studio demo
Module 2: Fundamentals of Large Language Models (LLMs)
- Core principles behind large language models
- The architectural design and operational mechanics of Gemini models
- A comparative analysis of Gemini versus GPT and other leading models
- Lab Session: Visualizing tokenization and model responses with sample prompts
Module 3: Initial Setup and Integration with Gemini
- Configuring the development environment
- Interacting 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 Various Gemini Models
- Investigating the diverse types and capabilities of Gemini models
- Selecting the optimal models for language, image, or multimodal tasks
- Initializing and testing generative model performance
- Practical Task: Evaluating and comparing text-to-text versus image-to-text model outputs
Module 5: Practical Applications and Real-World Scenarios
- Integrating Gemini AI into chat interfaces and Q&A systems
- Building tools for semantic search and content summarization
- Addressing ethical AI usage and bias considerations
- Group Project: Constructing a “Smart Research Assistant” leveraging NotebookLM and Gemini
Module 6: Advanced Features and Customization Strategies
- Optimizing prompts and handling advanced context
- Applying Gemini for code generation and debugging purposes
- Implementing fine-tuning workflows using Google Cloud Vertex AI
- Interactive Activity: Refining model responses through parameter adjustment and temperature control
Module 7: Real-World Projects and Team Collaboration
- Planning collaborative projects and setting up workflows
- Integrating Gemini AI with other Google platforms (Drive, Docs, Sheets)
- Team Exercise: Designing and deploying a compact AI application (such as a content summarizer, chatbot, or idea generator)
- Conducting peer reviews and discussing project outcomes
Module 8: Evaluation and Future Trajectories
- Resolving common issues in Gemini-based projects
- Reviewing the Gemini API roadmap and anticipated features
- Adopting best practices for AI governance and scalability
- Concluding Activity: Reflecting on practical insights and their application to career development
Summary and Recommended Next Steps
Requirements
- A foundational understanding of basic AI concepts
- Proficiency with APIs and cloud services
- Experience in Python programming
Target Audience
- Developers
- Data scientists
- AI enthusiasts
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