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: Foundations of AI and Google Gemini
- Defining Artificial Intelligence (AI)
- An overview of the Google Gemini AI ecosystem
- Distinguishing features and benefits of Gemini compared to alternative AI models
- Practical Exercise: Interactive exploration of Gemini AI via the Google AI Studio demonstration
Module 2: Insights into Large Language Models (LLMs)
- Core principles of large language models
- Understanding the internal architecture and functioning of Gemini models
- A comparative analysis of Gemini against GPT and other prominent models
- Laboratory Practice: Visualizing tokenization processes and model reactions through sample prompts
Module 3: Initiating Work with Gemini
- Preparing the development environment
- Interfacing with the Gemini API and SDK
- Managing authentication, tokens, and API keys
- Laboratory Exercise: Executing your first Gemini prompt using Python
Module 4: Utilizing Gemini Models
- Investigating various Gemini model categories and their capabilities
- Choosing the optimal models for linguistic, visual, or multimodal tasks
- Setup and testing of generative models
- Practical Task: Evaluating and contrasting outputs from text-to-text and image-to-text models
Module 5: Real-World Applications and Scenarios
- Embedding Gemini AI into chatbots and question-answering systems
- Building semantic search engines and content summarization utilities
- Considerations for ethical AI deployment and bias mitigation
- Collaborative Project: Constructing a “Smart Research Assistant” utilizing NotebookLM and Gemini
Module 6: Sophisticated Features and Customization
- Optimizing prompts and managing advanced context
- Leveraging Gemini for automated code generation and debugging
- Implementing fine-tuning processes with Google Cloud Vertex AI
- Interactive Exercise: Adjusting model responses through parameter configuration and temperature settings
Module 7: Professional Projects and Teamwork
- Strategic planning and workflow organization for collaborative projects
- Connecting Gemini AI with complementary Google tools (Drive, Docs, Sheets)
- Group Assignment: Designing and launching a compact AI application (such as a content summarizer, chatbot, or idea generator)
- Peer evaluation and discussion of project outcomes
Module 8: Assessment and Future Trajectories
- Diagnosing and resolving common issues in Gemini implementations
- Reviewing the Gemini API roadmap and anticipated features
- Best practices for AI governance and system scalability
- Concluding Session: Reflecting on key takeaways and their relevance to career development
Summary and Path Forward
Requirements
- Familiarity with fundamental AI concepts
- Prior experience with API interactions and cloud-based services
- Proficiency in Python programming
Target Audience
- Software Developers
- Data Scientists
- Enthusiasts of Artificial Intelligence
Testimonials (1)
Flow , vibe and topic on presentation
