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Duration 14 hours
Course Outline
Module 1: Foundations of AI and Google Gemini
- Defining Artificial Intelligence (AI)
- Introduction to Google Gemini AI and its broader ecosystem
- Distinguishing features and benefits of Gemini compared to other AI models
- Practical Exercise: Exploring Gemini AI capabilities via a Google AI Studio demonstration
Module 2: Deep Dive into Large Language Models (LLMs)
- Core principles of large language models
- Architectural design and operational mechanics of Gemini models
- Analysis of Gemini against GPT and other leading models
- Lab Session: Visualizing tokenization processes and model outputs using test prompts
Module 3: Initial Steps with Gemini
- Preparing the development environment
- Interacting with the Gemini API and SDK
- Managing authentication, tokens, and API keys
- Hands-on Session: Executing your first Gemini prompt via Python
Module 4: Utilizing Gemini Models
- Investigating various Gemini model types and their capabilities
- Choosing the right models for language, image, or multimodal tasks
- Initializing and testing generative models
- Applied Task: Evaluating outputs from text-to-text versus image-to-text models
Module 5: Real-World Applications and Scenarios
- Incorporating Gemini AI into chat and Q&A systems
- Building semantic search and summarization utilities
- Considerations for ethical AI usage and bias mitigation
- Team Assignment: Constructing a “Smart Research Assistant” using NotebookLM and Gemini
Module 6: Advanced Capabilities and Personalization
- Refining prompts and managing advanced context
- Leveraging Gemini for code creation and debugging
- Implementing fine-tuning workflows with Google Cloud Vertex AI
- Practical Task: Adjusting model responses through parameters and temperature settings
Module 7: Practical Projects and Teamwork
- Planning collaborative projects and establishing workflows
- Connecting Gemini AI with other Google services (Drive, Docs, Sheets)
- Group Challenge: Designing and deploying 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 Perspectives
- Resolving common challenges in Gemini projects
- Reviewing the Gemini API roadmap and anticipated features
- Adopting best practices for AI governance and scalability
- Closing Activity: Reflecting on practical takeaways and professional applications
Conclusions and Future Pathways
Requirements
- A foundational grasp of basic AI principles
- Familiarity with API usage and cloud-based services
- Programming experience in Python
Target Audience
- Software Developers
- Data Scientists
- AI Enthusiasts
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