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Duration 14 hours
Course Outline
Module 1: Introduction to AI and Google Gemini
- Defining Artificial Intelligence (AI)
- An overview of Google Gemini AI and its surrounding ecosystem
- Principal features and benefits of Gemini compared to other AI models
- Practical Activity: Exploring Gemini AI via the Google AI Studio demo
Module 2: Understanding Large Language Models (LLMs)
- Core principles of large language models
- The internal structure and functionality of Gemini models
- Comparison between Gemini and GPT, along with other prominent models
- Lab Practice: Visualizing tokenization and model outputs using example prompts
Module 3: Starting with Gemini
- Configuring the development environment
- Utilizing the Gemini API and SDK
- Handling authentication, tokens, and API keys
- Hands-on Lab: Executing your first Gemini prompt using Python
Module 4: Working with Gemini Models
- Investigating various Gemini model types and their capabilities
- Choosing suitable models for language, image, or multimodal tasks
- Initializing and testing generative models
- Exercise: Comparing outputs from text-to-text and image-to-text models
Module 5: Practical Applications and Use Cases
- Embedding Gemini AI into chat and question-answering applications
- Building semantic search and summarization utilities
- Considering ethical AI usage and bias mitigation
- Group Project: Creating a “Smart Research Assistant” using NotebookLM and Gemini
Module 6: Advanced Features and Customization
- Optimizing prompts and managing advanced context
- Leveraging Gemini for code generation and debugging
- Implementing fine-tuning workflows with Google Cloud Vertex AI
- Activity: Adjusting model responses through parameters 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 AI application (e.g., a content summarizer, chatbot, or idea generator)
- Conducting peer reviews and discussing project outcomes
Module 8: Evaluation and Future Directions
- Resolving common issues in Gemini projects
- Reviewing the Gemini API roadmap and anticipated features
- Best practices for AI governance and scalability
- Conclusion Activity: Reflecting on practical lessons learned and their career relevance
Summary and Next Steps
Requirements
- Familiarity with fundamental AI concepts
- Proficiency in using APIs and cloud services
- Knowledge of Python programming
Target Audience
- Software Developers
- Data Scientists
- Individuals interested in Artificial Intelligence
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