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Duration 14 hours
Course Outline
Module 1: Introduction to AI and Google Gemini
- Defining Artificial Intelligence (AI)
- Overview of Google Gemini AI and its ecosystem
- Key features and benefits of Gemini compared to other AI models
- Hands-on Activity: Exploring Gemini AI via the Google AI Studio demo
Module 2: Understanding Large Language Models (LLMs)
- Foundations of large language models
- Architecture and functionality of Gemini models
- Comparison of Gemini with GPT and other major models
- Practice Lab: Visualizing tokenization and model responses using sample prompts
Module 3: Initial Steps with Gemini
- Configuring the development environment
- Interacting with the Gemini API and SDK
- Authentication, tokens, and API key management
- Hands-on Lab: Executing your first Gemini prompt using Python
Module 4: Working with Gemini Models
- Examining various Gemini model types and their capabilities
- Selecting suitable models for language, image, or multimodal tasks
- Initializing and testing generative models
- Practical Exercise: Comparing outputs from text-to-text and image-to-text models
Module 5: Practical Applications and Use Cases
- Integrating Gemini AI into chat and Q&A systems
- Creating semantic search and summarization tools
- Ethical AI practices and bias considerations
- Group Project: Building a "Smart Research Assistant" using NotebookLM and Gemini
Module 6: Advanced Features and Customization
- Prompt optimization and advanced context management
- Leveraging Gemini for code generation and debugging
- Implementing fine-tuning workflows with Google Cloud Vertex AI
- Hands-on Activity: Tailoring model responses via parameters and temperature control
Module 7: Real-World Projects and Collaboration
- Planning collaborative projects and setting up workflows
- Integrating Gemini AI with other Google services (Drive, Docs, Sheets)
- Team Project: Designing and deploying a small AI application (e.g., 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 capabilities
- Best practices for AI governance and scalability
- Wrap-up Activity: Reflecting on practical lessons learned and career implications
Summary and Next Steps
Requirements
- Basic comprehension of AI principles
- Familiarity with APIs and cloud-based services
- Python programming skills
Target Audience
- Software developers
- Data scientists
- AI professionals and enthusiasts
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