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Course Outline
Module 1: Introduction to AI and Google Gemini
- Defining Artificial Intelligence (AI).
- An overview of Google Gemini AI and its surrounding ecosystem.
- Distinct features and benefits of Gemini compared to other AI models.
- Practical Activity: Exploring Gemini AI via the Google AI Studio demonstration.
Module 2: Understanding Large Language Models (LLMs)
- Core principles of large language models.
- The structure and functionality of Gemini models.
- A comparison of Gemini against GPT and other prominent models.
- Lab Practice: Visualizing tokenization and model responses using sample prompts.
Module 3: Getting Started with Gemini
- Configuring the development environment.
- Working 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 capabilities.
- Choosing suitable models for language, image, or multimodal tasks.
- Initializing and testing generative models.
- Practical Exercise: Analyzing and comparing text-to-text versus image-to-text model outputs.
Module 5: Practical Applications and Use Cases
- Embedding Gemini AI into chat and Q&A applications.
- Building semantic search and summarization tools.
- Considerations for ethical AI usage and bias mitigation.
- Group Project: Constructing a “Smart Research Assistant” using NotebookLM and Gemini.
Module 6: Advanced Features and Customization
- Prompt optimization and advanced context management.
- Utilizing Gemini for code generation and debugging tasks.
- Implementing fine-tuning workflows with Google Cloud Vertex AI.
- Practical Activity: Adjusting model responses through parameters and temperature control.
Module 7: Real-World Projects and Collaboration
- Planning collaborative projects and setting up 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 review 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 features.
- Best practices for AI governance and scalability.
- Wrap-up Activity: Reflecting on practical lessons and their application to career development.
Summary and Next Steps
Requirements
- Familiarity with foundational AI concepts.
- Experience working with APIs and cloud services.
- Proficiency in Python programming.
Target Audience
- Software developers.
- Data scientists.
- AI enthusiasts.
14 Hours
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