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Duration 14 hours
Course Outline
Module 1: Introduction to AI and Google Gemini
- Defining Artificial Intelligence (AI)
- Insights into Google Gemini AI and its broader ecosystem
- Principal features and benefits of Gemini compared to other AI models
- Practical Task: Discovering Gemini AI via the Google AI Studio demonstration
Module 2: Grasping Large Language Models (LLMs)
- Core principles of large language models
- The design and functionality of Gemini models
- A comparison between Gemini, GPT, and other top-tier models
- Lab Session: Visualizing tokenization and model responses through sample prompts
Module 3: Initial Steps with Gemini
- Preparing the development environment
- Interacting with the Gemini API and SDK
- Managing authentication, tokens, and API keys
- Lab Session: Executing your first Gemini prompt with Python
Module 4: Managing Gemini Models
- Examining various Gemini model types and their capabilities
- Choosing suitable models for language, image, or multimodal tasks
- Initiating and testing generative models
- Practical Task: Evaluating outputs from text-to-text and image-to-text models
Module 5: Real-World Applications and Scenarios
- Embedding Gemini AI into chat and Q&A platforms
- Creating tools for semantic search and summarization
- Considerations regarding ethical AI usage and bias
- Group Task: Constructing a “Smart Research Assistant” using NotebookLM and Gemini
Module 6: Advanced Capabilities and Customization
- Optimizing prompts and managing advanced context
- Leveraging Gemini for code generation and debugging
- Implementing fine-tuning workflows with Google Cloud Vertex AI
- Practical Task: Adjusting model responses via parameters and temperature settings
Module 7: Practical Projects and Teamwork
- Planning collaborative projects and setting up workflows
- Integrating Gemini AI with other Google services (Drive, Docs, Sheets)
- Team Task: Designing and launching a compact AI application (such as a content summarizer, chatbot, or idea generator)
- Peer evaluation and discussion of project outcomes
Module 8: Assessment and Future Trends
- Resolving common issues in Gemini projects
- Reviewing the Gemini API roadmap and forthcoming features
- Best practices for AI governance and scalability
- Final Activity: Reflecting on practical lessons learned and their career relevance
Overview and Subsequent Steps
Requirements
- Familiarity with fundamental AI principles
- Experience working with APIs and cloud services
- Proficiency in Python programming
Target Audience
- Developers
- Data scientists
- Individuals interested in AI
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 2600 € + VAT*
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