Synthetic Data in AI

 🧪 Synthetic Data in AI

🤖 Generate. Simulate. Train Smarter with Artificially Created Data.

📚 What You’ll Learn:


What is synthetic data?

→ Artificially generated data that mimics real-world datasets


Why it’s important in AI/ML:


Solves data scarcity issues


Helps with privacy & compliance


Enables model testing at scale


Supports balanced datasets


🛠️ Types of Synthetic Data:


Tabular data (structured datasets)


Image data (GANs, style transfer) 🖼️


Text data (language models, paraphrasing) ✍️


Time series data (simulation models) ⏱️


🔧 How It's Generated:


Generative Adversarial Networks (GANs)


Variational Autoencoders (VAEs)


Simulation-based tools


Rule-based synthetic generation


🧰 Tools & Libraries:


SDV (Synthetic Data Vault)


YData, scikit-learn, Faker


GAN-based frameworks (TensorFlow, PyTorch)


Unity & simulation engines (for 3D/robotics data)


👥 Ideal For:


Data Scientists & ML Engineers


Privacy-Conscious Organizations


AI Researchers


Developers needing scalable training data


📏 Use Cases:


Model training & validation


Data balancing


Privacy-safe analytics


AI for robotics, autonomous driving, and medical imaging


🕒 Duration: 1–2 Hours

📁 Includes: Code Notebooks, Tools Demo, Case Studies

🚀 When Real Data Isn’t Enough — Create What You Need.


👉 [Join the Session] | [Start Now] | [Download the Resources]


🎨 Design Suggestions:


Visuals:


Split between real and synthetic images or charts


GAN or neural network diagram


Icons for data, gear, shield (privacy), brain (AI)


Color Theme: Futuristic – teal, violet, black/white


Fonts: Tech-friendly (Orbitron, Inter, Roboto)

Learn Artificial Intelligence Course in Hyderabad

Read More

Feature Selection Techniques

Dealing with Imbalanced Datasets

Introduction to Data Augmentation

How to Label Data for Machine Learning

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