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Duration 14 hours
Course Outline
Module 1: Intro to AI and Google Gemini
- Defining Artificial Intelligence (AI)
- An overview of the Google Gemini AI landscape and its ecosystem
- Distinguishing features and benefits of Gemini compared to other AI models
- Practical Session: Interacting with Gemini AI via the Google AI Studio demo
Module 2: Grasping Large Language Models (LLMs)
- Core concepts of large language models
- The internal architecture and functioning of Gemini models
- Benchmarking Gemini against GPT and other leading models
- Practice Session: Observing tokenization and model outputs through sample prompts
Module 3: Initializing with Gemini
- Configuring the development environment
- Interacting with the Gemini API and SDK
- Managing authentication, tokens, and API keys
- Hands-on Lab: Executing your initial Gemini prompt using Python
Module 4: Utilizing Gemini Models
- Investigating various Gemini model types and their specific capabilities
- Choosing the right models for language, image, or multimodal tasks
- Setting up and evaluating generative models
- Applied Exercise: Analyzing the differences between text-to-text and image-to-text model outputs
Module 5: Practical Applications and Scenarios
- Embedding Gemini AI into chat and Q&A systems
- Crafting semantic search and summarization utilities
- Addressing ethical AI practices and bias mitigation
- Group Assignment: Creating a “Smart Research Assistant” leveraging NotebookLM and Gemini
Module 6: Advanced Features and Personalization
- Optimizing prompts and managing advanced context
- Applying Gemini for code creation and debugging
- Implementing fine-tuning processes with Google Cloud Vertex AI
- Practical Session: Adjusting model responses through parameter and temperature settings
Module 7: Real-World Projects and Teamwork
- Planning collaborative projects and establishing workflows
- Connecting Gemini AI with other Google services (Drive, Docs, Sheets)
- Team Challenge: Conceiving and launching a compact AI application (e.g., a content summarizer, chatbot, or idea generator)
- Conducting peer reviews and discussing project outcomes
Module 8: Assessment and Future Trajectories
- Resolving frequent issues in Gemini development projects
- Reviewing the Gemini API roadmap and forthcoming features
- Adhering to best practices for AI governance and scalability
- Closing Activity: Reflecting on practical insights and professional career applications
Recap and Subsequent Actions
Requirements
- A foundational grasp of basic AI principles
- Practical experience with APIs and cloud-based services
- Proficiency in Python programming
Target Audience
- Software Developers
- Data Scientists
- Individuals passionate about AI
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