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: AI Fundamentals and Google Gemini
- Defining Artificial Intelligence (AI)
- Introduction to the Google Gemini AI ecosystem
- Key features and advantages of Gemini compared to other models
- Hands-on Activity: Exploring Gemini AI via the Google AI Studio demo
Module 2: Deep Dive into Large Language Models (LLMs)
- Core principles 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: Initiating Work with Gemini
- Configuring the development environment
- Navigating the Gemini API and SDK
- Managing authentication, tokens, and API keys
- Hands-on Lab: Executing your first Gemini prompt using Python
Module 4: Utilizing Gemini Models
- Examining various Gemini model types and their capabilities
- Choosing the right 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
- Building semantic search and summarization tools
- Considerations for ethical AI usage and bias mitigation
- Group Project: Constructing a "Smart Research Assistant" using NotebookLM and Gemini
Module 6: Advanced Features and Customization
- Optimizing prompts and handling complex contexts
- Leveraging Gemini for code generation and debugging
- Implementing fine-tuning workflows via Google Cloud Vertex AI
- Hands-on Activity: Customizing model responses using parameters and temperature control
Module 7: Real-World Projects and Team 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 summarizer, 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: Reflecting on practical lessons learned and career applications
Summary and Next Steps
Requirements
- Familiarity with fundamental AI concepts
- Practical experience 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
