Description
Numerous sectors have been revolutionized machine learning (ML), which has made it possible to make decisions based on data and to automate processes. This book examines the whole machine learning pipeline, beginning with theoretical underpinnings and ending with implementation in the actual world. In this section, we discuss fundamental algorithms, methodologies for training models, assessment methods, and optimization strategies. There includes a comprehensive discussion on practical elements such as the preparation of data, the engineering of features, and the monitoring of hyperparameters. In addition, we examine the problems that pertain to the deployment of machine learning models, which include scalability, interpretability, and ethical considerations. Readers will be equipped with the abilities necessary to construct, assess, and deploy solid machine learning solutions in a variety of domains by reading this book, which bridges the gap between theory and actual application.
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