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Duration 14 hours
Course Outline
Module 1: Foundations of AI and Google Gemini
- Defining Artificial Intelligence (AI)
- Overview of the Google Gemini AI ecosystem
- Distinguishing features and benefits of Gemini compared to other AI models
- Practical Session: Investigating Gemini AI via the Google AI Studio demonstration
Module 2: The Mechanics of Large Language Models (LLMs)
- Core principles behind large language models
- Architectural details and operational logic of Gemini models
- Analysis of Gemini against GPT and other prominent models
- Lab Exercise: Observing tokenization and model outputs through sample prompts
Module 3: Initial Steps with Gemini
- Establishing the development environment
- Interacting with the Gemini API and SDK
- Managing authentication, tokens, and API credentials
- Hands-on Lab: Executing a first Gemini prompt using Python
Module 4: Utilizing Gemini Models
- Reviewing various Gemini model variants and their specific capabilities
- Choosing suitable models for language, imaging, or multimodal tasks
- Initializing and testing generative models
- Practical Task: Evaluating the differences between text-to-text and image-to-text model outputs
Module 5: Applied Scenarios and Case Studies
- Incorporating Gemini AI into chatbots and Q&A systems
- Creating tools for semantic search and content summarization
- Addressing ethical AI usage and bias mitigation
- Group Assignment: Developing a “Smart Research Assistant” leveraging NotebookLM and Gemini
Module 6: Advanced Capabilities and Personalization
- Refining prompts and managing advanced context handling
- Employing Gemini for code generation and error detection
- Implementing fine-tuning workflows via Google Cloud Vertex AI
- Practical Session: Adjusting model responses through parameter settings and temperature control
Module 7: Collaborative Real-World Projects
- Planning collaborative projects and establishing workflows
- Integrating Gemini AI with other Google services (Drive, Docs, Sheets)
- Team Assignment: 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: Assessment and Future Trajectories
- Resolving common issues in Gemini-based projects
- Reviewing the Gemini API roadmap and upcoming enhancements
- Adhering to best practices for AI governance and scalability
- Closing Activity: Reflecting on practical takeaways and career relevance
Summary and Recommended Next Steps
Requirements
- Familiarity with fundamental AI principles
- Proficiency in API integration and cloud-based services
- Background in Python programming
Target Audience
- Software Developers
- Data Scientists
- Professionals interested in AI technologies
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