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Course Outline
Module 1: Foundations of AI and Google Gemini
- Defining Artificial Intelligence (AI)
- Survey of the Google Gemini AI ecosystem
- Distinguishing features and benefits of Gemini compared to alternative AI models
- Practical Task: Interacting with Gemini AI via the Google AI Studio demonstration
Module 2: Insight into Large Language Models (LLMs)
- Core principles of large language models
- Analysis of Gemini model architecture and functionality
- Benchmarking Gemini against GPT and other prominent models
- Exercise Lab: Observing tokenization processes and model outputs using example prompts
Module 3: Initial Setup with Gemini
- Preparing the development environment
- Utilizing the Gemini API and SDK
- Handling authentication, tokens, and API keys
- Practical Lab: Executing the first Gemini prompt using Python
Module 4: Managing Gemini Models
- Reviewing various Gemini model varieties and their capabilities
- Choosing suitable models for linguistic, visual, or multimodal objectives
- Initialization and testing of generative models
- Practical Task: Evaluating differences between text-to-text and image-to-text model results
Module 5: Applied Scenarios and Use Cases
- Embedding Gemini AI into chat interfaces and Q&A systems
- Creating tools for semantic search and content summarization
- Considerations regarding ethical AI usage and bias
- Team Assignment: Constructing a “Smart Research Assistant” leveraging NotebookLM and Gemini
Module 6: Sophisticated Features and Personalization
- Optimizing prompts and managing complex contexts
- Applying Gemini for code creation and error resolution
- Implementing fine-tuning processes with Google Cloud Vertex AI
- Practical Task: Adjusting model outputs through parameter configuration and temperature regulation
Module 7: Applied Projects and Teamwork
- Planning collaborative projects and establishing workflows
- Connecting Gemini AI with the broader Google suite (Drive, Docs, Sheets)
- Team Assignment: Designing and launching a compact AI application, such as a content summarizer, chatbot, or idea generator
- Peer evaluation and analysis of project outcomes
Module 8: Assessment and Future Trajectories
- Resolving frequent challenges in Gemini implementations
- Investigating the Gemini API development roadmap and future capabilities
- Strategies for AI governance and scalability best practices
- Closing Activity: Reflecting on key practical insights and their application to professional careers
Conclusion and Subsequent Steps
Requirements
- A foundational grasp of core AI concepts
- Familiarity with API integration and cloud-based services
- Proficiency in Python programming
Target Audience
- Software Developers
- Data Scientists
- Enthusiasts of Artificial Intelligence
14 Hours
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