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Course Outline
Module 1: AI Fundamentals and Google Gemini Overview
- Defining Artificial Intelligence (AI).
- Introduction to the Google Gemini AI ecosystem.
- Key features and competitive 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 mechanisms of Gemini models.
- Comparative analysis of Gemini against GPT and other leading models.
- Practice Lab: Visualizing tokenization and model responses using sample prompts.
Module 3: Kickstarting with Gemini
- Configuring the development environment.
- Navigating the Gemini API and SDK.
- Managing authentication, tokens, and API keys.
- Hands-on Lab: Executing your first Gemini prompt using Python.
Module 4: Utilizing Gemini Models
- Exploring various Gemini model types and their specific 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: Real-World Applications and Use Cases
- Integrating Gemini AI into chatbots and Q&A systems.
- Creating tools for semantic search and content summarization.
- Addressing ethical AI usage and potential biases.
- Group Project: Constructing a “Smart Research Assistant” utilizing 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 parameter adjustments and temperature control.
Module 7: Collaborative Real-World Projects
- Planning collaborative workflows and project structures.
- Integrating Gemini AI with other Google services (Drive, Docs, Sheets).
- Team Project: Designing and deploying a mini AI application (e.g., content summarizer, chatbot, or idea generator).
- Conducting peer reviews and discussing project outcomes.
Module 8: Evaluation and Future Trends
- Troubleshooting common challenges in Gemini projects.
- Reviewing the Gemini API roadmap and upcoming features.
- Establishing best practices for AI governance and scalability.
- Wrap-up Activity: Reflecting on practical lessons learned and their career applications.
Summary and Next Steps
Requirements
- Familiarity with fundamental AI concepts.
- Practical experience with APIs and cloud services.
- Proficiency in Python programming.
Target Audience
- Software Developers.
- Data Scientists.
- AI Enthusiasts.
14 Hours
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