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Course Outline
Module 1: Introduction 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
- Hands-on Activity: Exploring Gemini AI via the Google AI Studio demonstration
Module 2: Understanding Large Language Models (LLMs)
- Core principles of large language models
- Architecture and operational mechanics of Gemini models
- Comparative analysis of Gemini against GPT and other leading models
- Practice Lab: Visualizing tokenization and model outputs using sample prompts
Module 3: Getting Started with Gemini
- Configuring the development environment
- Utilizing the Gemini API and SDK
- Managing authentication, tokens, and API keys
- Hands-on Lab: Executing the first Gemini prompt using Python
Module 4: Working with Gemini Models
- Investigating various Gemini model types and their capabilities
- Selecting suitable models for language, image, or multimodal tasks
- Initialization and testing of generative models
- Practical Exercise: Evaluating outputs from text-to-text and image-to-text models
Module 5: Practical Applications and Use Cases
- Integrating Gemini AI into chatbots and Q&A systems
- Creating semantic search and summarization tools
- Considerations for ethical AI usage and bias mitigation
- Group Project: Developing a “Smart Research Assistant” with NotebookLM and Gemini
Module 6: Advanced Features and Customization
- Prompt optimization and advanced context management
- Applying Gemini for code generation and debugging
- Fine-tuning processes using Google Cloud Vertex AI
- Hands-on Activity: Adjusting model responses through parameters and temperature settings
Module 7: Real-World Projects and Collaboration
- Planning collaborative projects and establishing workflows
- Integrating Gemini AI with other Google services (Drive, Docs, Sheets)
- Team Project: Designing and deploying a compact AI application (such as a content summarizer, chatbot, or idea generator)
- Peer review and discussion of project outcomes
Module 8: Evaluation and Future Directions
- Resolving common issues in Gemini projects
- Reviewing the Gemini API roadmap and upcoming enhancements
- Best practices for AI governance and scalability
- Wrap-up Activity: Reflecting on practical lessons and professional applications
Summary and Next Steps
Requirements
- Foundation in basic AI principles
- Proficiency with APIs and cloud services
- Experience in Python programming
Target Audience
- Software Developers
- Data Scientists
- AI Enthusiasts
14 Hours
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