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.
Duration 14 hours
Course Outline
Module 1: Introduction to AI and Google Gemini
- Defining Artificial Intelligence (AI)
- Exploring the Google Gemini AI ecosystem
- Distinguishing key features and benefits of Gemini compared to other AI models
- Hands-on Activity: Discovering Gemini AI capabilities via the Google AI Studio demo
Module 2: Understanding Large Language Models (LLMs)
- Core principles of large language models
- Architectural and operational insights into Gemini models
- Benchmarking Gemini against GPT and other prominent models
- Practice Lab: Visualizing tokenization processes and model responses with sample prompts
Module 3: Getting Started with Gemini
- Preparing the development environment
- Interacting with 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
- Examining various Gemini model types and their specific capabilities
- Choosing the right models for language, image, or multimodal tasks
- Initializing and testing generative models
- Practical Exercise: Analyzing differences in text-to-text versus image-to-text model outputs
Module 5: Practical Applications and Use Cases
- Embedding Gemini AI into chat and Q&A systems
- Creating semantic search and summarization tools
- Addressing ethical AI usage and bias considerations
- Group Project: Constructing a “Smart Research Assistant” leveraging NotebookLM and Gemini
Module 6: Advanced Features and Customization
- Optimizing prompts and handling advanced context
- Applying Gemini for code generation and debugging
- Implementing fine-tuning workflows with Google Cloud Vertex AI
- Hands-on Activity: Adjusting model responses through parameters and temperature settings
Module 7: Real-World Projects and Collaboration
- Planning collaborative projects and setting up workflows
- Integrating Gemini AI with other Google services (Drive, Docs, Sheets)
- Team Project: Designing and deploying a compact AI application (e.g., content summarizer, chatbot, or idea generator)
- Conducting peer reviews and discussing project outcomes
Module 8: Evaluation and Future Directions
- Resolving common challenges in Gemini projects
- Reviewing the Gemini API roadmap and forthcoming features
- Adopting best practices for AI governance and scalability
- Wrap-up Activity: Reflecting on key practical lessons and their career relevance
Summary and Next Steps
Requirements
- Foundational knowledge of core AI concepts
- Familiarity with APIs and cloud-based services
- Proficiency in Python programming
Target Audience
- Software developers
- Data scientists
- Enthusiasts and professionals interested in AI
Testimonials (1)
Flow , vibe and topic on presentation
