What will I learn in the Synthetic Data Generation & Use in AI course?

You will learn synthetic data generation for AI, including GANs, VAEs, diffusion models, data augmentation, applied AI workflows, hands-on projects, and practical use cases for machine learning model development.

Who should attend this synthetic data generation and AI course?

This course is suitable for students, researchers, AI learners, data science professionals, machine learning practitioners, generative AI learners, and participants interested in synthetic data for applied AI projects.

Does this course cover GANs, VAEs, and diffusion models?

Yes, the course covers GANs, VAEs, and diffusion models, explaining how these generative AI approaches can be used to create synthetic datasets, improve model training, and support applied machine learning workflows.

Is this course useful for hands-on AI projects and NSTC e-Certification?

Yes, the course is useful for learners interested in hands-on AI projects, synthetic data generation, generative model development, data-driven experimentation, applied machine learning, and earning an NSTC e-Certification.

Synthetic Data Generation & Use In AI

Attribute

Detail

Format

Online (e-LMS)

Level

Intermediate

Duration

3 Weeks

Certification

e-Certification + e-Marksheet

Tools

Gretel.ai, MOSTLY AI, SDV, TensorFlow, PyTorch, Hugging Face

About the Synthetic Data Generation 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

Prerequisites:

Frequently Asked Questions

1. What is the format of this Synthetic Data Generation & Use in AI course?

This is an Online (e-LMS) course delivered via our e-LMS platform. You will have access to pre-recorded video lectures, reading materials, assignments, quizzes, and hands-on projects that you can complete at your own pace.

2. Will I receive a certificate after completing this course?

Yes! Upon successful completion of all modules, assignments, and assessments, you will receive an e-Certification along with an e-Marksheet from NanoSchool (NSTC) that you can showcase on your CV and LinkedIn profile.

3. What are the prerequisites for this course?

Learners should have a foundational understanding of Artificial Intelligence concepts. Familiarity with basic tools and programming is recommended.

4. How long will I have access to the course materials?

You will have access to all course materials for the duration of 3 Weeks. The self-paced format allows you to learn according to your own schedule through our online learning management system.

5. Is mentor support available during the course?

Yes, dedicated mentor support is available throughout the course. You can reach out for doubt-clearing sessions, project guidance, and career advice related to Artificial Intelligence. Our mentors are industry experts and experienced professionals.

Enroll in Synthetic Data Generation & Use in AI today and take the next step in your professional journey. With expert-curated content, practical projects, and industry-recognized certification, this course is your gateway to mastering Artificial Intelligence skills that matter.

Brand	NSTC
Format	Online (e-LMS)
Duration	3 Weeks
Level	Intermediate
Domain	Artificial Intelligence
Hands-On	Yes – Practical projects with industrial datasets
Tools Used	Gretel.ai, MOSTLY AI, SDV, TensorFlow, PyTorch, Hugging Face, Diffusers, OpenAI API, Differential Privacy libraries

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Synthetic Data Generation & Use in AI