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
- Understanding 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)
- Fundamentals of large language models
- Architecture and operation of Gemini models
- Comparing Gemini with GPT and other leading models
- Practice Lab: Visualizing tokenization and model responses 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 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
- 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 applications
- Developing semantic search and summarization tools
- Considerations for ethical AI usage and bias mitigation
- Group Project: Create a “Smart Research Assistant” using NotebookLM and Gemini
Module 6: Advanced Features and Customization
- Prompt optimization and advanced context handling
- Utilizing Gemini for code generation and debugging
- Fine-tuning workflows with Google Cloud Vertex AI
- Hands-on Activity: Customizing 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 summarizer, chatbot, or idea generator)
- Peer review and discussion of project results
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
- Familiarity with fundamental AI concepts
- Experience working with APIs and cloud services
- Proficiency in Python programming
Target Audience
- Developers
- Data scientists
- AI enthusiasts
14 Hours
Testimonials (1)
Flow , vibe and topic on presentation
