Training Data for Machine Learning: Human Supervision from Annotation to Data Science

by Anthony Sarkis

Your training data has as much to do with the success of your data project as the algorithms themselves--most failures in deep learning systems relate to training data. But while training data is the foundation for successful machine learning, there are few comprehensive resources to help you ace the process. This hands-on guide explains how to work with and scale training data. Data science professionals and machine learning engineers will gain a solid understanding of the concepts, tools, and processes needed

329 pages, Paperback

Published December 19, 2023

Reviews

[Dean Matthews]

"Training Data for Machine Learning: Human Supervision from Annotation to Data Science" is an essential read for anyone looking to deepen their understanding of the crucial role that data plays in the field of machine learning. The book masterfully breaks down complex concepts into understandable segments, making it accessible not just for data scientists but for anyone intrigued by the intricacies of AI technology.

What sets this book apart is its practical approach, providing readers with actionable insights on how to implement effective data annotation (https://trainingdata.pro/data-annotation) strategies within their own projects. The detailed discussions on the ethics of data collection and the importance of maintaining accuracy and fairness in data annotation are timely and critical.

Overall, this book is not just informative but also incredibly engaging. Whether you're a seasoned data scientist or a curious enthusiast, this book will enrich your knowledge and perhaps inspire your next big project in the field of AI and machine learning. A must-read!