| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Intermediate |
| Duration | 3 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹10749 / $124 |
| Tools | Gretel.ai MOSTLY AI SDV TensorFlow PyTorch Hugging Face Diffusers OpenAI API Differential Privacy libraries |
About the Synthetic Data Generation & Use in AI Course
Synthetic Data Generation & Use in AI is an applied program designed for data scientists, ML engineers, and AI practitioners who face limitations with real-world datasets. The course explores how synthetic data—artificially generated but statistically accurate—can overcome data scarcity, improve privacy, and boost the robustness of AI models.
Over 3 intensive weeks, participants will master generation techniques including GANs, simulations, and diffusion models; evaluate data utility and privacy with industry-standard metrics; and apply synthetic data to real AI workflows across healthcare, finance, and autonomous systems. Unlock data innovation and learn to generate, simulate, and scale AI responsibly with synthetic data.
Program Highlights
• Comprehensive coverage of Synthetic Data Generation from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Artificial Intelligence
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Practical experience with tools: Gretel.ai, MOSTLY AI, SDV, TensorFlow
• Career-oriented training for academic and professional growth in Artificial Intelligence
Course Curriculum
Module 1: Introduction to Synthetic Data
- Define synthetic data and distinguish its types including tabular, image, text, and time-series formats
- Analyze the benefits of synthetic data over real data in terms of privacy, cost, and scalability
- Evaluate scenarios to determine when and when not to use synthetic data in AI projects
Module 2: Tools and Techniques for Data Generation
- Explore leading synthetic data generators including Gretel, MOSTLY AI, and SDV
- Implement GANs, VAEs, and LLMs for generating high-fidelity synthetic datasets
- Apply prompt-based data synthesis techniques for NLP and domain-specific tasks
Module 3: Generating Synthetic Data
- Build GAN-based generation pipelines for synthetic images and video content
- Generate synthetic tabular data using statistical models and simulation frameworks
- Balance and augment existing datasets with strategically synthesized samples
Module 4: Evaluation and Quality Assurance
- Measure utility metrics to assess how useful synthetic data is for downstream AI tasks
- Implement privacy metrics including differential privacy, k-anonymity, and membership inference tests
- Detect fidelity gaps, diversity limitations, and hidden biases in generated datasets
Module 5: Deploying Synthetic Data in AI Workflows
- Integrate synthetic data seamlessly into model training and validation pipelines
- Design augmentation strategies for low-data and imbalanced classification scenarios
- Conduct adversarial testing and model debugging using synthetic scenario generation
Module 6: Ethics, Governance, and Real-World Impact
- Navigate regulatory considerations and emerging industry standards for synthetic data use
- Practice transparency, disclosure, and responsible deployment in AI systems
- Complete a capstone project designing and evaluating a full synthetic data pipeline
Module 7: Advanced Generative Models and Diffusion Techniques
- Harness diffusion models for high-quality synthetic image and multimodal data generation
- Fine-tune large language models for domain-specific synthetic text corpus creation
- Optimize generative pipelines for computational efficiency and output quality
Tools, Techniques, or Platforms Covered
Gretel.ai MOSTLY AI SDV TensorFlow PyTorch Hugging Face Diffusers OpenAI API Differential Privacy libraries
Real-World Applications
- Apply Synthetic Data Generation skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical Artificial Intelligence competencies
- Solve industry-relevant problems using Synthetic Data Generation methodologies and tools
- Contribute to open-source projects and collaborative research in Artificial Intelligence
- Prepare for competitive examinations, interviews, and professional certifications in Artificial Intelligence
Who Should Attend & Prerequisites
- Industry-recognized e-Certification + e-Marksheet from NSTC
- Hands-on training with practical projects and industrial datasets
- Dedicated expert mentorship and doubt resolution
Certification
