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Implementing an Machine Learning algorithms is difficult. Algorithm descriptions may be incomplete, inconsistent, and distributed across a number of papers, chapters and even websites. This can result in varied interpretations of algorithms, undue attrition of algorithms, and ultimately bad science.
This book is an effort to address these issues by providing a handbook of algorithmic recipes drawn from the field of Machine Learning, described in a complete, consistent, and centralized manner. These standardized descriptions were carefully designed to be accessible, usable, and understandable.
An encyclopedic algorithm reference, this book is intended for research scientists, engineers, students, and interested amateurs. Each algorithm description provides a working code example in R.
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