Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Module 1: Introduction to AI and Google Gemini
- What is Artificial Intelligence (AI)?
- An 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
- The architecture and operational mechanics of Gemini models
- Comparing Gemini with GPT and other leading models
- Practice Lab: Visualising tokenisation and model responses using sample prompts
Module 3: Getting Started with Gemini
- Setting up the development environment
- Working with the Gemini API and SDK
- Authentication, tokens, and API keys
- Hands-on Lab: Executing your first Gemini prompt using Python
Module 4: Working with Gemini Models
- Exploring various Gemini model types and capabilities
- Selecting suitable models for language, image, or multimodal tasks
- Initialising and testing generative models
- Practical Exercise: Comparing text-to-text and image-to-text model outputs
Module 5: Practical Applications and Use Cases
- Integrating Gemini AI into chat and Q&A applications
- Developing semantic search and summarisation tools
- Ethical AI usage and bias considerations
- Group Project: Build a “Smart Research Assistant” using NotebookLM and Gemini
Module 6: Advanced Features and Customisation
- Prompt optimisation and advanced context handling
- Utilising Gemini for code generation and debugging
- 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
- Collaborative project planning and workflow setup
- Integrating Gemini AI with other Google tools (Drive, Docs, Sheets)
- Team Project: Design and deploy 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
- Exploring the Gemini API roadmap and upcoming features
- Best practices for AI governance and scalability
- Wrap-up Activity: Reflection on practical lessons learned and career applications
Summary and Next Steps
Requirements
- A grasp of fundamental AI concepts
- Experience with APIs and cloud services
- Proficiency in Python programming
Audience
- Developers
- Data scientists
- AI enthusiasts
14 Hours
Testimonials (1)
Flow , vibe and topic on presentation