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Duration 14 hours
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
- Hands-on Activity: Exploring Gemini AI capabilities via the Google AI Studio demonstration
Module 2: Understanding Large Language Models (LLMs)
- Core principles of large language models
- Architecture and operational mechanics of Gemini models
- Benchmarking Gemini against GPT and other leading models
- Practice Lab: Visualizing tokenization and model outputs using sample prompts
Module 3: Getting Started with Gemini
- Configuring the development environment
- Interacting with the Gemini API and SDK
- Managing authentication, tokens, and API keys
- Hands-on Lab: Executing your first Gemini prompt using Python
Module 4: Working with Gemini Models
- Examining various Gemini model types and their specific capabilities
- Selecting the optimal model for language, image, or multimodal tasks
- Initialization and testing of generative models
- Practical Exercise: Analyzing differences between text-to-text and image-to-text model outputs
Module 5: Practical Applications and Use Cases
- Embedding Gemini AI into chat and Q&A systems
- Creating semantic search and text summarization tools
- Addressing ethical AI usage and potential biases
- Group Project: Constructing a “Smart Research Assistant” leveraging NotebookLM and Gemini
Module 6: Advanced Features and Customization
- Refining prompt engineering and advanced context management
- Applying Gemini to code generation and debugging tasks
- Implementing fine-tuning workflows with Google Cloud Vertex AI
- Hands-on Activity: Adjusting model responses through parameter settings and temperature control
Module 7: Real-World Projects and Collaboration
- Planning collaborative projects and establishing workflows
- Integrating Gemini AI with other Google tools (Drive, Docs, Sheets)
- Team Project: Designing and deploying a small-scale AI application (e.g., content summarizer, chatbot, or idea generator)
- Peer evaluation and discussion of project outcomes
Module 8: Evaluation and Future Directions
- Resolving common issues encountered in Gemini projects
- Reviewing the Gemini API roadmap and anticipated future features
- Adopting best practices for AI governance and system scalability
- Wrap-up Activity: Reflecting on key practical lessons and their career implications
Summary and Next 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
- Technology Enthusiasts
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