All Courses

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

Quiz
Beta

Chapter: Basic Methods for Data Generation

Test your understanding and practice the concepts from this chapter

Quiz Instructions

This quiz contains 10 questions to help you practice.
You need to score at least 70% to pass.
Attempts: Unlimited.
Your highest score will be kept.
Please attempt this quiz without assistance; however, feel free to refer to the chapter notes or use a code interpreter if needed.
This assessment is for your learning and understanding, It is not formally accredited (yet).

Question Format

The questions are designed to be engaging, focusing on understanding, application, and interpretation rather than rote memorization. Expect scenario-based problems that test your ability to apply what you've learned.

Attempts

Best scores and quiz attempts will appear.

© 2025 ApX Machine Learning