Generative AI for Data Synthesis Training Course
Generative AI is a branch of artificial intelligence dedicated to producing novel content, data, and models that accurately mirror real-world distributions. Synthetic data serves as a viable alternative to real-world data.
This instructor-led, live training, available both online and onsite, is designed for intermediate-level data professionals who need to generate synthetic datasets for AI model training, particularly in scenarios where real data is limited or sensitive.
Upon completing this training, participants will be equipped to:
- Grasp the significance and creation process of synthetic data within Generative AI.
- Deploy Generative AI models to generate high-quality synthetic data.
- Evaluate the quality and utility of synthetic datasets.
- Navigate the ethical and legal implications associated with using synthetic data.
- Apply synthetic data strategies to solve real-world AI challenges.
Course Format
- Interactive lectures and discussions.
- Numerous exercises and practical sessions.
- Hands-on implementation in a live laboratory environment.
Customization Options
- To arrange a tailored training session for this course, please reach out to us.
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
- A foundational understanding of machine learning concepts.
- Proficiency in Python programming.
- Familiarity with data science workflows.
Target Audience
- Data scientists.
- AI practitioners.
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
Generative AI for Data Synthesis Training Course - Enquiry
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)
Adam - GE Aerospace Poland Sp. z o.o.
Course - Prompt Engineering for ChatGPT
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