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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

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

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

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

Chapter 5: Evaluating Synthetic Data Quality

Importance of Evaluation

Visual Inspection Methods

Basic Statistical Comparisons

Comparing Data Distributions

Concept of Fidelity vs. Utility

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

Introduction to Synthetic Data for Machine Learning

Prerequisites: Basic familiarity with Python programming, including NumPy for numerical operations.

Level: Beginner

What You'll Learn

Synthetic Data Fundamentals
Define synthetic data and explain its role in addressing data limitations in machine learning.
Data Generation Principles
Understand the basic principles behind creating artificial data that mimics real-world data properties.
Simple Generation Techniques
Apply basic methods to generate synthetic data, including rule-based systems and statistical sampling.
Tabular Data Synthesis
Learn techniques specifically for generating synthetic structured (tabular) data.
Image Data Synthesis Basics
Understand introductory methods for creating synthetic image data.
Synthetic Data Evaluation
Recognize the importance of evaluating synthetic data quality and learn basic assessment methods.
Common Tools Awareness
Become aware of common software libraries used for generating synthetic data.

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