Introduction to Synthetic Data for Machine Learning
Chapter 1: Understanding Synthetic Data
What is Synthetic Data?
Why Generate Artificial Data?
Real Data vs. Synthetic Data
Common Terminology
Potential Benefits
General Limitations
Quiz for Chapter 1
Chapter 2: Basic Methods for Data Generation
The Idea of Data Generation Models
Generating Data from Statistical Distributions
Introduction to Rule-Based Systems
Generating Simple Numerical Data
Generating Simple Categorical Data
Hands-on Practical: Create Basic Synthetic Data
Quiz for Chapter 2
Chapter 3: Generating Synthetic Tabular Data
Understanding Tabular Data Structure
Row Sampling Techniques
Independent Column Value Generation
Preserving Basic Column Correlations
Introduction to Data Anonymization Concepts
Hands-on Practical: Generate a Synthetic Table
Quiz for Chapter 3
Chapter 4: Introduction to Synthetic Image Data
Why Synthetic Data for Images?
Basic Image Properties: Pixels and Color
Creating Images with Simple Shapes and Patterns
Applying Noise and Simple Augmentations
Introduction to Rendering Simple Scenes
Challenges in Realistic Image Generation
Hands-on Practical: Generate Simple Synthetic Images
Quiz for Chapter 4
Chapter 5: Evaluating Synthetic Data Quality
Importance of Evaluation
Visual Inspection Methods
Basic Statistical Comparisons
Comparing Data Distributions
Concept of Fidelity vs. Utility
Quiz for Chapter 5
Chapter 6: Tools and Libraries Overview
Role of Software in Data Generation
Libraries for Basic Data Manipulation (NumPy, Pandas)
Introduction to Faker Library
Libraries for Simple Image Manipulation (Pillow, Scikit-image)
Finding Generation Tools
Quiz for Chapter 6
Libraries for Simple Image Manipulation (Pillow, Scikit-image)
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- Pillow Documentation, Pillow Contributors, 2024 - Official documentation for the Pillow library, offering comprehensive guides and API references for basic image manipulation in Python.
- scikit-image Documentation, The scikit-image team, 2025 - Official documentation for scikit-image, providing detailed information on its algorithms for image processing with NumPy arrays.
- scikit-image: image processing in Python, Stéfan van der Walt, Johannes L. Schönberger, Juan Nunez-Iglesias, François Boulogne, Joshua D. Warner, Nicolae Y. Galli, Andrea S. Giugliano, Timothy D. Nathaniel, Olav Alex Eklund, Alexander R. Sofroniew, Maarten Becker, Louis Bonnett, Harald Möller, Eric Ben Shushan, Christian von Faber, Catherine R. Herhold, and Tony Yu, 2014 PeerJ, Vol. 2 DOI: 10.7717/peerj.453 - The original academic paper introducing the scikit-image library, outlining its architecture and functionalities for image processing in Python.
- Hands-On Image Processing with Python, Sandipan Dey, 2018 (Packt Publishing) - A practical book guiding readers through image processing with Python, including usage examples for libraries like Pillow and scikit-image.
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