Preface
Machine learning (ML) has made our lives far easier. We cannot imagine our world without ML-based products and services. ML models need to be trained on large-scale datasets to perform well. However, collecting and annotating real data is extremely expensive, error-prone, and subject to privacy issues, to name a few disadvantages. Synthetic data is a promising solution to real-data ML-based solutions.
Synthetic Data for Machine Learning is a unique book that will help you master synthetic data, designed to make your learning journey enjoyable. In this book, theory and good practice complement each other to provide leading-edge support!
The book helps you to overcome real data issues and improve your ML models’ performance. It provides an overview of the fundamentals of synthetic data generation and discusses the pros and cons of each approach. It reveals the secrets of synthetic data and the best practices to leverage it better.
By the end of this book, you will have mastered synthetic data and increased your chances of becoming a market leader. It will enable you to springboard into a more advanced, cheaper, and higher-quality data source, making you well prepared and ahead of your peers for the next generation of ML!
