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Course Outline
Module 1: Intro to AI and Google Gemini
- Defining Artificial Intelligence (AI)
- Overview of the Google Gemini AI ecosystem
- Distinctive features and benefits of Gemini compared to other AI models
- Practical Task: Interacting with Gemini AI via the Google AI Studio demonstration
Module 2: Exploring Large Language Models (LLMs)
- Core principles of large language models
- Architectural and operational aspects of Gemini models
- Comparison of Gemini against GPT and other major models
- Lab Exercise: Observing tokenization and model outputs using sample prompts
Module 3: Initial Setup with Gemini
- Establishing the development environment
- Utilizing the Gemini API and SDK
- Managing authentication, tokens, and API keys
- Hands-on Lab: Executing your first Gemini prompt in Python
Module 4: Managing Gemini Models
- Reviewing various Gemini model types and their capabilities
- Choosing suitable models for language, image, or multimodal tasks
- Setting up and testing generative models
- Exercise: Analyzing differences between text-to-text and image-to-text model outputs
Module 5: Real-World Applications and Scenarios
- Incorporating Gemini AI into chat and question-answering systems
- Creating tools for semantic search and content summarization
- Addressing ethical AI usage and bias management
- Team Task: Creating a "Smart Research Assistant" leveraging NotebookLM and Gemini
Module 6: Advanced Capabilities and Personalization
- Optimizing prompts and managing complex contexts
- Employing Gemini for code creation and debugging
- Implementing fine-tuning workflows using Google Cloud Vertex AI
- Practical Task: Adjusting model responses through parameters and temperature settings
Module 7: Practical Projects and Teamwork
- Planning collaborative projects and setting up workflows
- Connecting Gemini AI with other Google services (Drive, Docs, Sheets)
- Group Assignment: Designing and launching a compact AI application (such as a content summarizer, chatbot, or idea generator)
- Peer evaluation and discussion of project outcomes
Module 8: Assessment and Future Outlook
- Resolving common issues in Gemini projects
- Reviewing the Gemini API roadmap and future features
- Best practices for AI governance and scalability
- Conclusion Activity: Reflecting on practical insights gained and their career relevance
Recap and Future Steps
Requirements
- Familiarity with fundamental AI principles
- Proficiency with APIs and cloud-based services
- Programming experience in Python
Target Audience
- Software developers
- Data scientists
- Professionals interested in AI
14 Hours
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