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Course Outline
Introduction to Generative AI
- Defining generative AI
- Overview of generative models (GANs, VAEs, etc.)
- Applications and case studies
The Need for Synthetic Data
- Limitations of real data
- Privacy and security concerns
- Enhancing AI model robustness
Generating Synthetic Data
- Techniques for synthetic data generation
- Ensuring data quality and diversity
- Practical workshop: Creating your first synthetic dataset
Evaluating Synthetic Data
- Metrics for assessing synthetic data quality
- Comparing synthetic vs. real data performance
- Case study analysis
Ethical and Legal Aspects
- Navigating the ethical landscape
- Legal frameworks and compliance
- Balancing innovation with responsibility
Advanced Topics in Data Synthesis
- Synthetic data for unsupervised learning
- Cross-domain data synthesis
- Future trends in generative AI
Capstone Project
- Applying knowledge to real-world scenarios
- Developing a synthetic data strategy
- Assessment and feedback
Summary and Next Steps
Requirements
- An understanding of basic machine learning concepts
- Experience with Python programming
- Familiarity with data science workflows
Audience
- Data scientists
- AI practitioners
21 Hours
Testimonials (2)
Going over the various use cases and application of AI was helpful. I enjoyed the walkthrough of the various AI Agents.
Axel Schulz - CANARIE Inc
Course - Microsoft 365 Copilot: AI Productivity Across Word, Excel, PowerPoint, Outlook, and Teams
I liked that trainer had a lot of knowledge and shared it with us
