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. The theories, notations, and proofs are of not much interest and use to the programming community and Machine Learning practitioners. 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. The book attempts to explain the concepts in a very lucid manner with appropriate case studies and simple mathematical illustrations wherever possible. Machine Learning techniques are growing rapidly, and researchers are developing new algorithms and techniques to maximize the model performance. The new techniques for evaluation and validation, etc., are covered. This book takes a much-needed holistic approachdputting all together with an in-depth treatise of a multidisciplinary applications of Machine Learning. The book covers a comprehensive overview of the state-of-the-art with help of real-life problems and applications. The book is unique because it caters to basic concepts and applications of the role of statistics, exploratory data analysis, and Machine Learning.

Statistical Modeling in Machine Learning: Concepts and Applications will help statisticians, Machine Learning practitioners and programmers solving various tasks such as classification, regression, clustering, forecasting, recommending and more.

Provides a comprehensive overview of the state-of-the-art in statistical concepts applied to Machine Learning with the help of real-life problems, applications and tutorials
Presents a step-by-step approach from fundamentals to advanced techniques
Includes Case Studies with both successful and unsuccessful applications of Machine Learning to understand challenges in its implementation, along with worked examples