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Course Outline
Module 1: Overview of AI and Google Gemini
- Defining Artificial Intelligence (AI)
- Introduction to Google Gemini AI and its broader ecosystem
- Distinct features and benefits of Gemini compared to other AI models
- Practical Activity: Investigating Gemini AI via the Google AI Studio demonstration
Module 2: Foundations of Large Language Models (LLMs)
- Core principles of large language models
- Internal structure and functioning of Gemini models
- Benchmarking Gemini against GPT and other top-tier models
- Practice Session: Visualizing tokenization and model outputs using sample prompts
Module 3: Initial Steps with Gemini
- Configuring the development workspace
- Interacting with the Gemini API and SDK
- Handling authentication, tokens, and API keys
- Hands-on Lab: Executing your first Gemini prompt using Python
Module 4: Managing Gemini Models
- Exploring various Gemini model types and their capabilities
- Choosing the right models for language, image, or multimodal tasks
- Initializing and testing generative models
- Practical Task: Analyzing differences between text-to-text and image-to-text model outputs
Module 5: Real-World Applications and Scenarios
- Integrating Gemini AI into chatbots and Q&A systems
- Creating tools for semantic search and content summarization
- Considerations for ethical AI usage and bias mitigation
- Group Challenge: Constructing a “Smart Research Assistant” utilizing NotebookLM and Gemini
Module 6: Advanced Capabilities and Personalization
- Optimizing prompts and handling complex contexts
- Applying Gemini for code creation and debugging
- Implementing fine-tuning workflows with Google Cloud Vertex AI
- Hands-on Activity: Modifying model responses using parameters and temperature settings
Module 7: Practical Projects and Teamwork
- Planning collaborative projects and establishing workflows
- Connecting Gemini AI with other Google services (Drive, Docs, Sheets)
- Team Assignment: Designing and launching a small-scale AI application (e.g., content summarizer, chatbot, or idea generator)
- Peer assessment and discussion of project outcomes
Module 8: Assessment and Future Prospects
- Resolving common challenges in Gemini implementations
- Reviewing the Gemini API roadmap and forthcoming features
- Best practices for AI governance and scalability
- Closing Activity: Reflecting on key takeaways and career implications
Summary and Path Forward
Requirements
- A solid grasp of fundamental AI principles
- Familiarity with API development and cloud services
- Proficiency in Python programming
Target Audience
- Software Developers
- Data Scientists
- Individuals with a keen interest in AI
14 Hours
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