The machine learning (ML) revolution has made our world unimaginable without its products and services. However, training ML models requires vast datasets, which entails a process plagued by high costs, errors, and privacy concerns associated with collecting and annotating real data. Synthetic data emerges as a promising solution to all these challenges.
This book is designed to bridge theory and practice of using synthetic data, offering invaluable support for your ML journey. Synthetic Data for Machine Learning empowers you to tackle real data issues, enhance your ML models' performance, and gain a deep understanding of synthetic data generation. You’ll explore the strengths and weaknesses of various approaches, gaining practical knowledge with hands-on examples of modern methods, including Generative Adversarial Networks (GANs) and diffusion models. Additionally, you’ll uncover the secrets and best practices to harness the full potential of synthetic data.
By the end of this book, you’ll have mastered synthetic data and positioned yourself as a market leader, ready for more advanced, cost-effective, and higher-quality data sources, setting you ahead of your peers in the next generation of ML.
publication date
2023-10-27
what you will learn
Understand real data problems, limitations, drawbacks, and pitfalls
Harness the potential of synthetic data for data-hungry ML models
Discover state-of-the-art synthetic data generation approaches and solutions
Uncover synthetic data potential by working on diverse case studies
Understand synthetic data challenges and emerging research topics
Apply synthetic data to your ML projects successfully
key features
Avoid common data issues by identifying and solving them using synthetic data-based solutions * Master synthetic data generation approaches to prepare for the future of machine learning * Enhance performance, reduce budget, and stand out from competitors using synthetic data * Purchase of the print or Kindle book includes a free PDF eBook
approach
An easy-to-follow guide that addresses machine learning problems, data-related issues, and the deficiency of real data-based solutions, through to synthetic data generation approaches and solutions. At each step, you will learn and master theoretical and practical aspects of the topic supported by examples, case studies, thorough discussions, and insights into the future.
audience
If you are a machine learning (ML) practitioner or researcher who wants to overcome data problems, this book is for you. Basic knowledge of ML and Python programming is required. The book is one of the pioneer works on the subject, providing leading-edge support for ML engineers, researchers, companies, and decision makers.
short description
Machine learning is rapidly transitioning to synthetic data solutions, yet comprehensive resources to understand its uses are scarce. Synthetic Data for Machine Learning is a pioneering, comprehensive book covering synthetic data generation approaches, case studies, best practices, hands-on examples, and above all, how to use it to solve your own problems.
subtitle
Revolutionize your approach to machine learning with this comprehensive conceptual guide
keywords
Data science, Hands-on machine learning, Machine learning book, Data science books, Data science for business
Product ISBN
9781803245409
