Statistical Modeling in Machine Learning

Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and rolesof statistics, exploratory data analysis and Machine Learning, and their application in various domains. The various aspects of Machine Learning are discussed along with basics of statistics in the first half of the book. Concepts are presentedwith simple examplesandgraphical representation for better understanding of techniques. Scientists, researchers, academicians, and research scholars working in the field of predictive analytics using Machine Learning and statistics can benefit from case studies of real-world applications from different domains, depicted in the latter half of the book.The book starts with fundamentals, mathematical pre-requisites and traditional approaches in order to provide readers with a solid foundation of understanding. Then advanced techniques and applications are presented along with challenges and future requirements in the field. Statistical Modeling in Machine Learning: Concepts and Applications takes a much-needed holistic approach –putting key concepts together with an in-depth treatise on multi-disciplinary applications of Machine Learning. The book is useful to readers from three backgrounds –statisticians, programmers, and Machine Learning practitioners, who are applying Machine Learning to solve various tasks such as classification, predictive analytics, regression, clustering, recommending, and more.New case studiesand research problemstatements will also be discussed, which will help researchers in their application of the concepts of statistics and Machine Learning. 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