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Course Outline
Introduction to Generative AI
- Definition and scope of generative AI.
- Survey of generative models, including GANs and VAEs.
- Exploration of applications and case studies.
The Need for Synthetic Data
- Limitations inherent in real-world data.
- Privacy and security implications.
- Strategies for enhancing AI model robustness.
Generating Synthetic Data
- Techniques for synthesizing data.
- Ensuring diversity and quality in generated datasets.
- Hands-on workshop: Building your first synthetic dataset.
Evaluating Synthetic Data
- Metrics for measuring synthetic data quality.
- Performance comparison between synthetic and real data.
- In-depth case study analysis.
Ethical and Legal Aspects
- Navigating the ethical landscape of AI.
- Understanding legal frameworks and compliance requirements.
- Balancing innovation with ethical responsibility.
Advanced Topics in Data Synthesis
- Synthetic data applications in unsupervised learning.
- Cross-domain data synthesis techniques.
- Emerging trends in generative AI.
Capstone Project
- Applying acquired skills to realistic scenarios.
- Formulating a comprehensive synthetic data strategy.
- Project assessment and constructive feedback.
Summary and Next Steps
Requirements
- Solid understanding of fundamental machine learning principles.
- Practical experience in Python programming.
- Familiarity with standard data science workflows.
Intended Audience
- Data scientists.
- AI practitioners.
21 Hours
Testimonials (2)
the tips and recommended prompts that we can take away from this training
Lee Mei Lin - ST Engineering IHQ Pte Ltd
Course - InVideo AI: Creating Engaging Short-Form Videos
use of proper and effective prompt
