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Course Outline
Module 1: Introduction to AI and Google Gemini
- Defining Artificial Intelligence (AI) in a modern context.
- An overview of the Google Gemini AI ecosystem.
- Distinct features and competitive advantages of Gemini compared to other AI models.
- Practical Session: Investigating Gemini AI capabilities via the Google AI Studio demonstration.
Module 2: Understanding Large Language Models (LLMs)
- Core principles underlying large language models.
- Architectural design and operational mechanics of Gemini models.
- A comparative analysis of Gemini against GPT and other prominent models.
- Exercises: Observing tokenization processes and model responses through sample prompts.
Module 3: Initiating Work with Gemini
- Establishing the necessary development environment.
- Interacting with the Gemini API and Software Development Kit (SDK).
- Managing authentication, access tokens, and API keys.
- Laboratory Exercise: Executing a foundational Gemini prompt using Python.
Module 4: Operating Gemini Models
- Reviewing various Gemini model variants and their specific capabilities.
- Selecting the optimal model for language, image, or multimodal requirements.
- Initializing and testing generative model performance.
- Applied Exercise: Analyzing differences between text-to-text and image-to-text model outputs.
Module 5: Practical Applications and Scenarios
- Incorporating Gemini AI into chatbots and question-answering systems.
- Creating tools for semantic search and content summarization.
- Addressing ethical AI practices and bias mitigation strategies.
- Group Assignment: Constructing a “Smart Research Assistant” utilizing NotebookLM and Gemini.
Module 6: Advanced Features and Customization
- Refining prompt engineering and advanced context management.
- Leveraging Gemini for code creation and debugging tasks.
- Implementing fine-tuning workflows via Google Cloud Vertex AI.
- Practical Session: Adjusting model responses through parameter settings and temperature controls.
Module 7: Real-World Projects and Teamwork
- Planning collaborative projects and establishing efficient workflows.
- Connecting Gemini AI with the broader Google suite (Drive, Docs, Sheets).
- Team Assignment: Designing and launching a compact AI application, such as a content summarizer, chatbot, or idea generator.
- Conducting peer reviews and discussing project outcomes.
Module 8: Assessment and Future Trajectories
- Resolving common challenges encountered in Gemini projects.
- Reviewing the Gemini API development roadmap and anticipated features.
- Adopting best practices for AI governance and system scalability.
- Conclusion: Reflecting on key practical lessons and their relevance to career progression.
Overview and Future Actions
Requirements
- A solid grasp of fundamental AI principles
- Practical experience with API integration and cloud-based services
- Proficiency in Python programming
Target Audience
- Software developers
- Data science professionals
- Individuals with a strong interest in AI technologies
14 Hours
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