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Course Outline
Module 1: Fundamentals of AI and Google Gemini
- Defining Artificial Intelligence (AI)
- An overview of the Google Gemini AI ecosystem
- Distinguishing features of Gemini compared to other AI models
- Practical Task: Interacting with Gemini AI via the Google AI Studio demonstration
Module 2: Deep Dive into Large Language Models (LLMs)
- Core principles of large language models
- How Gemini models are architected and function
- Benchmarking Gemini against GPT and other top-tier models
- Lab Session: Visualizing tokenization and model outputs with sample prompts
Module 3: Initial Steps with Gemini
- Configuring the development workspace
- Interfacing with the Gemini API and SDK
- Managing authentication, tokens, and API keys
- Hands-on Lab: Executing a first Gemini prompt with Python
Module 4: Leveraging Gemini Models
- Reviewing various Gemini model types and their strengths
- Selecting the right models for text, image, or multimodal tasks
- Setting up and testing generative capabilities
- Exercise: Analyzing differences between text-to-text and image-to-text outputs
Module 5: Real-World Applications
- Embedding Gemini AI in chatbots and Q&A systems
- Building semantic search and content summarization tools
- Navigating ethical AI usage and bias mitigation
- Group Task: Creating a “Smart Research Assistant” with NotebookLM and Gemini
Module 6: Advanced Capabilities and Personalization
- Refining prompts and managing complex contexts
- Utilizing Gemini for code generation and error resolution
- Optimizing workflows via Google Cloud Vertex AI
- Activity: Adjusting model responses through parameter and temperature settings
Module 7: Collaborative Real-World Projects
- Planning team projects and establishing workflows
- Connecting Gemini AI with Google Drive, Docs, and Sheets
- Team Challenge: Designing and launching a mini AI app (e.g., summarizer, chatbot, or idea generator)
- Evaluating and discussing project outcomes with peers
Module 8: Assessment and Future Trends
- Resolving common challenges in Gemini implementations
- Reviewing the Gemini API roadmap and emerging features
- Best practices for AI governance and system scalability
- Conclusion: Reflecting on key takeaways and career implications
Overview and Future Actions
Requirements
- Familiarity with fundamental AI principles
- Hands-on experience with APIs and cloud platforms
- Competence in Python programming
Target Learners
- Software developers
- Data scientists
- Professionals passionate about AI
14 Hours
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