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Duration 14 hours
Course Outline
Module 1: Foundations of AI and Google Gemini
- Defining Artificial Intelligence (AI)
- Overview of the Google Gemini AI ecosystem
- Distinct features and competitive advantages of Gemini compared to other AI models
- Practical Session: Exploring Gemini AI capabilities via the Google AI Studio demo
Module 2: Deep Dive into Large Language Models (LLMs)
- Core principles of large language models
- Architectural insights into Gemini model operations
- Comparative analysis of Gemini against GPT and other industry-leading models
- Lab Exercise: Visualizing tokenization processes and model responses using sample prompts
Module 3: Initiating Development with Gemini
- Establishing a robust development environment
- Utilizing the Gemini API and SDKs
- Managing authentication, tokens, and API keys
- Development Lab: Executing the first Gemini prompt using Python
Module 4: Leveraging Gemini Model Variants
- Examining the diverse types and capabilities of Gemini models
- Selecting optimal models for language, image, or multimodal tasks
- Initializing and testing generative model performance
- Applied Exercise: Analyzing differences between text-to-text and image-to-text model outputs
Module 5: Practical Implementation and Scenarios
- Integrating Gemini AI into chatbots and Q&A systems
- Creating semantic search and content summarization tools
- Addressing ethical AI usage and bias mitigation
- Collaborative Project: Developing a “Smart Research Assistant” using NotebookLM and Gemini
Module 6: Advanced Features and Model Customization
- Optimizing prompts and handling advanced contexts
- Employing Gemini for code generation and debugging tasks
- Implementing fine-tuning workflows with Google Cloud Vertex AI
- Technical Activity: Refining model responses through parameter and temperature control
Module 7: Real-World Projects and Team Collaboration
- Planning collaborative projects and establishing workflows
- Integrating Gemini AI with broader Google tools (Drive, Docs, Sheets)
- Team Challenge: 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 Trajectories
- Resolving common challenges in Gemini projects
- Reviewing the Gemini API roadmap and forthcoming features
- Adopting best practices for AI governance and scalability
- Concluding Session: Reflecting on key takeaways and their application to career development
Recap and Recommended Next Steps
Requirements
- Familiarity with fundamental AI concepts
- Practical experience with APIs and cloud-based services
- Proficiency in Python programming
Target Audience
- Software Developers
- Data Scientists
- Professionals with a strong interest in AI
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