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Название: Statistical Modeling in Machine Learning: Concepts and Applications
Автор: Tilottama Goswami, G.R. Sinha
Издательство: Academic Press, Elsevier
Год: 2023
Страниц: 398
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and roles of statistics, exploratory data analysis and Machine Learning. The various aspects of Machine Learning (ML) are discussed along with basics of statistics. Concepts are presented with simple examples and graphical representation for better understanding of techniques. This book takes a holistic approach – putting key concepts together with an in-depth treatise on multi-disciplinary applications of Machine Learning. New case studies and research problem statements are discussed, which will help researchers in their application areas based on the concepts of statistics and Machine Learning. The knowledge of statistics is considered as prerequisite for in-depth understanding of Machine Learning. The existing books on statistics most of the time cater to readers from mathematics and statistics backgrounds. This book will be useful to statisticians, programmers, Machine Learning practitioners, and all those who apply Machine Learning to the benefit of innovating and automating to solve various Machine Learning tasks such as classification, predictive analytics, regression, clustering, recommending, etc.
Автор: Tilottama Goswami, G.R. Sinha
Издательство: Academic Press, Elsevier
Год: 2023
Страниц: 398
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and roles of statistics, exploratory data analysis and Machine Learning. The various aspects of Machine Learning (ML) are discussed along with basics of statistics. Concepts are presented with simple examples and graphical representation for better understanding of techniques. This book takes a holistic approach – putting key concepts together with an in-depth treatise on multi-disciplinary applications of Machine Learning. New case studies and research problem statements are discussed, which will help researchers in their application areas based on the concepts of statistics and Machine Learning. The knowledge of statistics is considered as prerequisite for in-depth understanding of Machine Learning. The existing books on statistics most of the time cater to readers from mathematics and statistics backgrounds. This book will be useful to statisticians, programmers, Machine Learning practitioners, and all those who apply Machine Learning to the benefit of innovating and automating to solve various Machine Learning tasks such as classification, predictive analytics, regression, clustering, recommending, etc.