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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 technological ecosystem
- Key benefits and distinct features of Gemini compared to other AI models
- Practical Task: Exploring Gemini AI capabilities via the Google AI Studio demonstration
Module 2: Grasping Large Language Models (LLMs)
- Core principles of large language models
- Understanding the architecture and functional mechanics of Gemini models
- A comparative analysis of Gemini against GPT and other industry-leading models
- Practice Session: Observing tokenization and model outputs using selected prompts
Module 3: Initial Steps with Gemini
- Establishing the required development environment
- Interacting with the Gemini API and software development kits
- Managing authentication, tokens, and API keys
- Lab Work: Executing an initial Gemini prompt utilizing Python
Module 4: Utilizing Gemini Models
- Investigating various Gemini model categories and their respective capabilities
- Selecting suitable models for language, image, or multimodal processing tasks
- Setting up and evaluating generative models
- Applied Exercise: Evaluating differences in text-to-text and image-to-text model results
Module 5: Real-World Applications and Scenarios
- Incorporating Gemini AI into chat interfaces and Q&A systems
- Crafting tools for semantic search and content summarization
- Considering ethical AI practices and bias mitigation
- Team Assignment: Creating a “Smart Research Assistant” by leveraging NotebookLM and Gemini
Module 6: Advanced Capabilities and Tailoring
- Optimizing prompts and managing complex context handling
- Applying Gemini for code generation and error debugging
- Implementing fine-tuning processes via Google Cloud Vertex AI
- Practical Task: Adjusting model behavior through parameter settings and temperature controls
Module 7: Practical Projects and Teamwork
- Planning collaborative projects and establishing workflows
- Connecting Gemini AI with additional Google services (Drive, Docs, Sheets)
- Group Project: Conceptualizing and launching a compact AI solution (such as a content summarizer, chatbot, or idea generator)
- Conducting peer reviews and analyzing project outcomes
Module 8: Assessment and Future Prospects
- Addressing common challenges in Gemini development
- Reviewing the Gemini API roadmap and anticipated new features
- Adopting best practices for AI governance and system scalability
- Concluding Session: Reflecting on key takeaways and their professional applications
Conclusion and Further Steps
Requirements
- Familiarity with fundamental AI principles
- Practical knowledge of APIs and cloud-based services
- Proficiency in Python programming
Target Audience
- Software developers
- Data scientists
- Individuals with a strong interest in AI technology
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