Synthetic Data Generation & Use in AI
Unlock Data Innovation—Generate, Simulate, and Scale AI with Synthetic Data.
Early access to e-LMS included
About This Course
Synthetic Data Generation & Use in AI is an applied course designed for data scientists, ML engineers, and AI practitioners who face limitations with real-world datasets. The program explores how synthetic data—artificially generated but statistically accurate—can overcome data scarcity, improve privacy, and boost the robustness of AI models. Participants will learn generation techniques (GANs, simulations, diffusion models), evaluate data utility and privacy, and apply synthetic data to real AI workflows.
Aim
To equip learners with the theoretical understanding and practical skills needed to generate, validate, and deploy synthetic data for AI development—enhancing model training, privacy protection, and data diversity in low-data or sensitive environments.
Program Objectives
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To enable secure, bias-mitigated data innovation using synthetic data
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To reduce reliance on costly, restricted, or imbalanced real-world datasets
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To build competency in cutting-edge generative models and simulation tools
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To promote responsible AI through privacy-first data practices
Program Structure
Week 1: Foundations of Synthetic Data and Its Role in AI
Module 1: Introduction to Synthetic Data
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Chapter 1.1: What is Synthetic Data?
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Chapter 1.2: Types of Synthetic Data (Tabular, Image, Text, Time-Series)
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Chapter 1.3: Benefits Over Real Data – Privacy, Cost, Scalability
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Chapter 1.4: When (and When Not) to Use Synthetic Data in AI
Module 2: Tools and Techniques for Data Generation
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Chapter 2.1: Overview of Synthetic Data Generators (Gretel, MOSTLY AI, SDV)
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Chapter 2.2: Using GANs, VAEs, and LLMs for Synthetic Data
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Chapter 2.3: Prompt-Based Data Synthesis for NLP Tasks
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Chapter 2.4: Preprocessing Real Data for Synthetic Modeling
Week 2: Building and Validating Synthetic Data Pipelines
Module 3: Generating Synthetic Data
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Chapter 3.1: GAN-based Generation for Images and Video
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Chapter 3.2: Synthetic Tabular Data with Statistical Models
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Chapter 3.3: Balancing and Augmenting Datasets with Synthetic Samples
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Chapter 3.4: Using LLMs to Generate Domain-Specific Text Data
Module 4: Evaluation and Quality Assurance
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Chapter 4.1: Utility Metrics – How “Useful” is Synthetic Data?
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Chapter 4.2: Privacy Metrics – Differential Privacy, k-Anonymity, Membership Inference
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Chapter 4.3: Fidelity, Diversity, and Bias Detection
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Chapter 4.4: Comparing Synthetic vs. Real Model Performance
Week 3: Operationalization, Ethics, and Use Cases
Module 5: Deploying Synthetic Data in AI Workflows
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Chapter 5.1: Integrating Synthetic Data in Model Training Pipelines
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Chapter 5.2: Augmentation Strategies in Low-Data and Imbalanced Settings
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Chapter 5.3: Model Debugging and Adversarial Testing with Synthetic Scenarios
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Chapter 5.4: Federated Learning and Simulation Environments
Module 6: Ethics, Governance, and Real-World Impact
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Chapter 6.1: Regulatory Considerations and Industry Standards
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Chapter 6.2: Transparency, Disclosure, and Responsible Use
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Chapter 6.3: Use Cases: Healthcare, Finance, Autonomous Systems
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Chapter 6.4: Capstone Project – Design and Evaluate a Synthetic Data Pipeline
Who Should Enrol?
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Data scientists, ML/AI engineers, researchers, and data engineers
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Professionals in healthcare, finance, robotics, or sensitive data domains
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Knowledge of Python, ML frameworks, and basic statistics is recommended
Program Outcomes
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Master the generation of high-fidelity, domain-specific synthetic datasets
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Understand legal and ethical implications of synthetic data
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Apply synthetic data to improve AI model robustness and reduce bias
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Evaluate privacy-preserving techniques for safe data deployment
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Integrate synthetic data into production-grade ML pipelines
Fee Structure
Discounted: ₹21499 | $249
We accept 20+ global currencies. View list →
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
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