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Course Outline
Module 1: AI Fundamentals and Google Gemini
- Defining Artificial Intelligence (AI)
- Introduction to Google Gemini AI and its broader ecosystem
- Distinctive features and advantages of Gemini compared to other AI models
- Hands-on Activity: Exploring Gemini AI via the Google AI Studio demo
Module 2: Deep Dive into Large Language Models (LLMs)
- Core principles of large language models
- Architecture and operational mechanics of Gemini models
- Comparative analysis of Gemini against GPT and other leading models
- Practice Lab: Visualizing tokenization and model responses with sample prompts
Module 3: Launching with Gemini
- Configuring the development environment
- Navigating the Gemini API and SDK
- Managing authentication, tokens, and API keys
- Hands-on Lab: Executing your initial Gemini prompt using Python
Module 4: Utilizing Gemini Models
- Examining various Gemini model types and their capabilities
- Choosing the right models for language, image, or multimodal tasks
- Initializing and testing generative models
- Practical Exercise: Comparing outputs from text-to-text and image-to-text models
Module 5: Practical Applications and Use Cases
- Integrating Gemini AI into chat and question-and-answer applications
- Building semantic search and summarization tools
- Addressing ethical AI usage and bias mitigation
- Group Project: Developing a "Smart Research Assistant" using NotebookLM and Gemini
Module 6: Advanced Features and Customization
- Optimizing prompts and handling complex contexts
- Leveraging Gemini for code generation and debugging
- Implementing fine-tuning workflows via Google Cloud Vertex AI
- Hands-on Activity: Tailoring model responses through parameters and temperature control
Module 7: Real-World Projects and Collaboration
- Planning collaborative projects and establishing workflows
- Integrating Gemini AI with other Google tools such as Drive, Docs, and Sheets
- Team Project: Designing and deploying a small-scale AI application (e.g., content summarizer, chatbot, or idea generator)
- Peer review and discussion of project outcomes
Module 8: Evaluation and Future Directions
- Troubleshooting common issues encountered in Gemini projects
- Exploring the Gemini API roadmap and anticipated features
- Adopting best practices for AI governance and scalability
- Wrap-up Activity: Reflecting on practical lessons learned and their career relevance
Summary and Next Steps
Requirements
- Familiarity with fundamental AI concepts
- Experience with APIs and cloud services
- Proficiency in Python programming
Target Audience
- Software Developers
- Data Scientists
- AI Enthusiasts
14 Hours
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