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
- Defining Artificial Intelligence (AI)
- Overview of the Google Gemini AI ecosystem
- Distinctive features and advantages 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)
- Core principles of large language models
- Architecture and operational mechanics of Gemini models
- Comparison of 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 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 applications
- Developing semantic search and summarization tools
- Considerations for ethical AI usage and bias
- Group Project: Creating 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: Customizing model responses through 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-scale 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
- Examining the Gemini API roadmap and forthcoming 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
- Foundational knowledge of basic AI concepts
- Proficiency with APIs and cloud services
- Experience in Python programming
Target Audience
- Software Developers
- Data Scientists
- Enthusiasts of Artificial Intelligence
14 Hours
Testimonials (1)
Flow , vibe and topic on presentation