INTERPRETABLE MACHINE LEARNING

Machine learning has great potential for improving products, processes, and research. But computers usually do not explain their predictions which is a barrier to the adoption of machine learning. Ideal for students or developers who need an introduction to modeling processes and/or predictive models, this machine learning textbook provides a comprehensive breakdown of the entire modeling process. This book is about making machine learning models and their decisions interpretable. This machine learning textbook is commonly cited throughout the machine learning community for its conceptual framework and diverse range of topics. This textbook can function as both a casual intro to machine learning for a reference book for anyone needing to brush up on neural networks, testing methods, and more. The book actively encourages the reader every step of the way to conduct their own experiments and investigations on their own, making it useful for developing the skills and curiosities needed to make relevant breakthroughs within a machine learning capacity/career.