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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
- 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)
- The basics of large language models
- How Gemini models are architected and operate
- Comparing Gemini with GPT and other leading models
- Practice Lab: Visualising tokenisation and model responses using sample prompts
Module 3: Getting Started with Gemini
- Configuring the development environment
- Working with the Gemini API and SDK
- Managing authentication, tokens, and API keys
- Hands-on Lab: Running your first Gemini prompt using Python
Module 4: Working with Gemini Models
- Exploring various Gemini model types and their capabilities
- Selecting the right models for language, image, or multimodal tasks
- Initialising 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 applications
- Developing tools for semantic search and summarisation
- Considering ethical AI usage and bias
- Group Project: Building a “Smart Research Assistant” using NotebookLM and Gemini
Module 6: Advanced Features and Customisation
- Optimising prompts and handling advanced context
- Utilising Gemini for code generation and debugging
- Implementing fine-tuning workflows with Google Cloud Vertex AI
- Hands-on Activity: Customising model responses using parameters and temperature control
Module 7: Real-World Projects and Collaboration
- Planning collaborative projects and setting up workflows
- Integrating Gemini AI with other Google tools (Drive, Docs, Sheets)
- Team Project: Designing and deploying a small AI application (e.g., content summariser, chatbot, or idea generator)
- Peer review and discussion of project outcomes
Module 8: Evaluation and Future Directions
- Troubleshooting common issues in Gemini projects
- Reviewing the Gemini API roadmap and upcoming features
- Best practices for AI governance and scalability
- Wrap-up Activity: Reflecting on practical lessons learned and their career applications
Summary and Next Steps
Requirements
- A solid grasp of basic AI concepts
- Familiarity with APIs and cloud services
- Experience with Python programming
Target Audience
- Developers
- Data scientists
- AI enthusiasts
14 Hours
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