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Course Outline
Module 1: Introduction to AI and Google Gemini
- Defining Artificial Intelligence (AI).
- An overview of the Google Gemini AI ecosystem.
- Key features and advantages of Gemini compared to other AI models.
- Hands-on Activity: Exploring Gemini AI via the Google AI Studio demo.
Module 2: Understanding Large Language Models (LLMs)
- Fundamentals of large language models.
- Architecture and operational mechanics of Gemini models.
- Comparing Gemini with GPT and other leading models.
- Practice Lab: 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.
- Authentication, tokens, and managing API keys.
- Hands-on Lab: Executing your first Gemini prompt using Python.
Module 4: Working with Gemini Models
- Exploring various Gemini model types and their capabilities.
- Selecting the appropriate models for language, image, or multimodal tasks.
- Initializing and testing generative models.
- Practical Exercise: Comparing outputs from text-to-text and image-to-text models.
Module 5: Practical Applications and Use Cases
- Integrating Gemini AI into chat and Q&A applications.
- Developing tools for semantic search and text summarization.
- Ethical considerations in AI usage, including bias mitigation.
- Group Project: Building a “Smart Research Assistant” using NotebookLM and Gemini.
Module 6: Advanced Features and Customization
- Prompt optimization and handling advanced context.
- Utilizing Gemini for code generation and debugging.
- Fine-tuning workflows via Google Cloud Vertex AI.
- Hands-on Activity: Customizing model responses by adjusting parameters and temperature control.
Module 7: Real-World Projects and Collaboration
- Collaborative project planning and workflow setup.
- Integrating Gemini AI with other Google tools such as Drive, Docs, and Sheets.
- Team Project: Designing and deploying a small AI application (e.g., content summarizer, chatbot, or idea generator).
- Peer review and discussion of project outcomes.
Module 8: Evaluation and Future Directions
- Troubleshooting common issues in Gemini projects.
- Exploring the Gemini API roadmap and upcoming features.
- Best practices for AI governance and scalability.
- Wrap-up Activity: Reflecting on practical lessons learned and potential career applications.
Summary and Next Steps
Requirements
- Fundamental understanding of AI concepts
- Experience working with APIs and cloud services
- Proficiency in Python programming
Target Audience
- Developers
- Data scientists
- AI enthusiasts
14 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 3200 € + VAT*
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