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Course Outline
Module 1: Introduction to AI and Google Gemini
- Defining Artificial Intelligence (AI)
- Overview of the Google Gemini AI ecosystem
- Distinctive features and benefits of Gemini compared to other AI models
- Practical Session: Investigating Gemini AI via the Google AI Studio demonstration
Module 2: Comprehending Large Language Models (LLMs)
- Core principles of large language models
- Architectural and operational aspects of Gemini models
- Comparison of Gemini with GPT and other leading AI models
- Lab Exercise: Visualizing tokenization and model reactions using sample prompts
Module 3: Initial Steps with Gemini
- Configuring the development environment
- Utilizing the Gemini API and SDK
- Managing authentication, tokens, and API keys
- Practical Lab: Executing your first Gemini prompt using Python
Module 4: Utilizing Gemini Models
- Examining various Gemini model types and their capabilities
- Choosing suitable models for language, image, or multimodal tasks
- Initiating and evaluating generative models
- Exercise: Analyzing differences between text-to-text and image-to-text model outputs
Module 5: Applied Solutions and Use Cases
- Integrating Gemini AI into chatbots and Q&A platforms
- Creating semantic search and summarization utilities
- Addressing ethical AI usage and bias implications
- Group Task: Developing a “Smart Research Assistant” leveraging NotebookLM and Gemini
Module 6: Advanced Capabilities and Customization
- Optimizing prompts and managing advanced context
- Leveraging Gemini for code generation and debugging
- Implementing fine-tuning workflows via Google Cloud Vertex AI
- Practical Session: Adjusting model responses using parameters and temperature controls
Module 7: Real-World Projects and Teamwork
- Planning collaborative projects and establishing workflows
- Connecting 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: Assessment and Future Trajectories
- Resolving common issues within Gemini projects
- Reviewing the Gemini API roadmap and anticipated features
- Best practices for AI governance and scalability
- Conclusion Activity: Reflecting on practical insights and professional career applications
Recap and Future 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
- Enthusiasts of AI technology
14 Hours
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