Machine Learning and Noisy Labels: Definitions, Theory, Techniques and Solutions provides an ideal introduction to Machine Learning with noisy labels that is suitable for senior undergraduates, post graduate students, researchers and practitioners using, and researching, Machine Learning methods. Most of the modern Machine Learning models based on Deep Learning techniques depend on carefully curated and cleanly labeled training sets to be reliably trained and deployed. However, the expensive labeling process involved in the acquisition of such training sets limits the number and size of datasets available to build new models, slowing down progress in the field. This book defines the different types of label noise, introduces the theory behind the problem, presents the main techniques that enable the effective use of noisy-label training sets, and explains the most accurate methods. The book starts by defining the noisy-label learning problem, then it introduces different types of label noise and the theory behind the problem. It also presents the main techniques and methods that enable the effective use of noisy-label training sets. With this book, the reader will have the tools to be able to understand, reproduce, and design regression, classification, segmentation, and detection models that can be trained with large-scale noisy-label training sets. Advanced Machine Learning courses that cover topics in learning with noisy-label and real-world datasets will greatly benefit from the insights provided in this book.