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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 versus 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
- A foundational understanding of machine learning concepts.
- Practical experience with Python programming.
- Familiarity with data science workflows.
Audience
- Data scientists.
- AI practitioners.
21 Hours
Testimonials (2)
The interactive style, the exercises
Tamas Tutuntzisz
Course - Introduction to Prompt Engineering
A great repository of resources for future use, instructor's style (full of good sense of humor, great level of detail)
