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: AI Fundamentals and Google Gemini
- Defining Artificial Intelligence (AI)
- Introduction to the Google Gemini AI ecosystem
- Distinct features and competitive advantages of Gemini compared to other models
- Practical Task: Interacting with Gemini AI via the Google AI Studio demonstration
Module 2: Large Language Models (LLMs) Explained
- Core principles of large language models
- Analysis of Gemini model architecture and functionality
- Benchmarking Gemini against GPT and other industry-leading models
- Lab Exercise: Observing tokenization and model responses using test prompts
Module 3: Initiating Work with Gemini
- Configuring the development environment
- Utilizing the Gemini API and SDK
- Managing authentication, tokens, and API keys
- Hands-on Session: Executing an initial Gemini prompt using Python
Module 4: Operating Gemini Models
- Investigating various Gemini model variants and their capabilities
- Choosing the optimal model for language, image, or multimodal processing
- Initialization and testing of generative models
- Applied Task: Evaluating differences between text-to-text and image-to-text outputs
Module 5: Real-World Applications and Scenarios
- Embedding Gemini AI into chatbots and Q&A systems
- Creating tools for semantic search and content summarization
- Addressing ethical AI practices and bias mitigation
- Group Assignment: Developing a “Smart Research Assistant” by combining NotebookLM and Gemini
Module 6: Advanced Capabilities and Customization
- Optimizing prompts and managing complex context
- Leveraging Gemini for code generation and debugging tasks
- Implementing fine-tuning workflows via Google Cloud Vertex AI
- Practical Task: Adjusting model behavior through parameters and temperature settings
Module 7: Industry Projects and Teamwork
- Structuring collaborative project planning and workflows
- Connecting Gemini AI with other Google platforms (Drive, Docs, Sheets)
- Team Assignment: Designing and deploying a compact AI application, such as a content summarizer, chatbot, or idea generator
- Conducting peer reviews and discussing project outcomes
Module 8: Assessment and Future Trajectories
- Resolving common challenges in Gemini-based projects
- Reviewing the Gemini API roadmap and anticipated features
- Adopting best practices for AI governance and scalability
- Concluding Activity: Reflecting on key takeaways and their professional applications
Recap and Recommended Next Steps
Requirements
- Familiarity with fundamental AI principles
- Hands-on experience with APIs and cloud-based services
- Proficiency in Python programming
Target Audience
- Software Developers
- Data Scientists
- Professionals interested in AI technologies
Testimonials (1)
Flow , vibe and topic on presentation
