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Duration 14 hours
Course Outline
Module 1: Introduction to AI and Google Gemini
- Defining Artificial Intelligence (AI)
- Overview of the Google Gemini AI ecosystem
- Distinct features and benefits of Gemini compared to other AI models
- Practical Session: Discovering Gemini AI via the Google AI Studio demonstration
Module 2: Understanding Large Language Models (LLMs)
- Core principles of large language models
- Architectural and operational aspects of Gemini models
- Comparison between Gemini and GPT or other top-tier models
- Lab Practice: Visualizing tokenization and model outputs using sample prompts
Module 3: Initiating Work 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 Gemini Models
- Investigating various Gemini model types and their capabilities
- Choosing suitable models for language, image, or multimodal tasks
- Initializing and testing generative models
- Exercise: Evaluating outputs from text-to-text and image-to-text models
Module 5: Practical Applications and Scenarios
- Incorporating Gemini AI into chat interfaces and Q&A systems
- Creating semantic search and summarization utilities
- Considering ethical AI usage and bias mitigation
- Group Project: Constructing a “Smart Research Assistant” leveraging NotebookLM and Gemini
Module 6: Advanced Features and Customization
- Optimizing prompts and managing complex contexts
- Employing Gemini for code generation and debugging tasks
- Implementing fine-tuning workflows via Google Cloud Vertex AI
- Activity: Modifying model responses through parameter and temperature control
Module 7: Real-World Projects and Teamwork
- 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)
- Conducting peer reviews and discussing project outcomes
Module 8: Evaluation and Future Prospects
- Resolving common challenges in Gemini projects
- Reviewing the Gemini API roadmap and anticipated features
- Adopting best practices for AI governance and scalability
- Conclusion: Reflecting on key practical takeaways and their relevance to career development
Summary and Next Steps
Requirements
- Familiarity with fundamental AI principles
- Proficiency in working with APIs and cloud services
- Background in Python programming
Target Audience
- Software Developers
- Data Scientists
- AI Enthusiasts
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