Statistical Prediction Machine Learning by Chen John (22 results)

Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Condition: As New. Unread book in perfect condition.

Hardback. Condition: New. New copy - Usually dispatched within 4 working days. 585.

Condition: New.

Condition: New.

Condition: New.

Condition: As New. Unread book in perfect condition.

Hardcover. Condition: new. Hardcover. Written by an experienced statistics educator and two data scientists, this book unifies conventional statistical thinking and contemporary machine learning framework into a single overarching umbrella over data science. The book is designed to bridge the knowledge gap between conventional statistics and machine learning. It provides an accessible approach for readers with a basic statistics background to develop a mastery of machine learning. The book starts with elucidating examples in Chapter 1 and fundamentals on refined optimization in Chapter 2, which are followed by common supervised learning methods such as regressions, classification, support vector machines, tree algorithms, and range regressions. After a discussion on unsupervised learning methods, it includes a chapter on unsupervised learning and a chapter on statistical learning with data sequentially or simultaneously from multiple resources.One of the distinct features of this book is the comprehensive coverage of the topics in statistical learning and medical applications. It summarizes the authors teaching, research, and consulting experience in which they use data analytics. The illustrating examples and accompanying materials heavily emphasize understanding on data analysis, producing accurate interpretations, and discovering hidden assumptions associated with various methods.Key Features:Unifies conventional model-based framework and contemporary data-driven methods into a single overarching umbrella over data science.Includes real-life medical applications in hypertension, stroke, diabetes, thrombolysis, aspirin efficacy.Integrates statistical theory with machine learning algorithms.Includes potential methodological developments in data science. This book unifies conventional statistical thinking and contemporary machine learning framework into a single overarching umbrella over data science. The book is designed to bridge the knowledge gap between conventional statistics and machine learning. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condition: New. In.

Condition: New. 1st edition NO-PA16APR2015-KAP.

Condition: New.

hardcover. Condition: New. 1st Edition. Special order direct from the distributor.

Hardcover. Condition: Brand New. 320 pages. 9.19x6.13x9.21 inches. In Stock.

Hardcover. Condition: new. Hardcover. Written by an experienced statistics educator and two data scientists, this book unifies conventional statistical thinking and contemporary machine learning framework into a single overarching umbrella over data science. The book is designed to bridge the knowledge gap between conventional statistics and machine learning. It provides an accessible approach for readers with a basic statistics background to develop a mastery of machine learning. The book starts with elucidating examples in Chapter 1 and fundamentals on refined optimization in Chapter 2, which are followed by common supervised learning methods such as regressions, classification, support vector machines, tree algorithms, and range regressions. After a discussion on unsupervised learning methods, it includes a chapter on unsupervised learning and a chapter on statistical learning with data sequentially or simultaneously from multiple resources.One of the distinct features of this book is the comprehensive coverage of the topics in statistical learning and medical applications. It summarizes the authors teaching, research, and consulting experience in which they use data analytics. The illustrating examples and accompanying materials heavily emphasize understanding on data analysis, producing accurate interpretations, and discovering hidden assumptions associated with various methods.Key Features:Unifies conventional model-based framework and contemporary data-driven methods into a single overarching umbrella over data science.Includes real-life medical applications in hypertension, stroke, diabetes, thrombolysis, aspirin efficacy.Integrates statistical theory with machine learning algorithms.Includes potential methodological developments in data science. This book unifies conventional statistical thinking and contemporary machine learning framework into a single overarching umbrella over data science. The book is designed to bridge the knowledge gap between conventional statistics and machine learning. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condition: new. Hardcover. Written by an experienced statistics educator and two data scientists, this book unifies conventional statistical thinking and contemporary machine learning framework into a single overarching umbrella over data science. The book is designed to bridge the knowledge gap between conventional statistics and machine learning. It provides an accessible approach for readers with a basic statistics background to develop a mastery of machine learning. The book starts with elucidating examples in Chapter 1 and fundamentals on refined optimization in Chapter 2, which are followed by common supervised learning methods such as regressions, classification, support vector machines, tree algorithms, and range regressions. After a discussion on unsupervised learning methods, it includes a chapter on unsupervised learning and a chapter on statistical learning with data sequentially or simultaneously from multiple resources.One of the distinct features of this book is the comprehensive coverage of the topics in statistical learning and medical applications. It summarizes the authors teaching, research, and consulting experience in which they use data analytics. The illustrating examples and accompanying materials heavily emphasize understanding on data analysis, producing accurate interpretations, and discovering hidden assumptions associated with various methods.Key Features:Unifies conventional model-based framework and contemporary data-driven methods into a single overarching umbrella over data science.Includes real-life medical applications in hypertension, stroke, diabetes, thrombolysis, aspirin efficacy.Integrates statistical theory with machine learning algorithms.Includes potential methodological developments in data science. This book unifies conventional statistical thinking and contemporary machine learning framework into a single overarching umbrella over data science. The book is designed to bridge the knowledge gap between conventional statistics and machine learning. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

HRD. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Written by an experienced statistics educator and two data scientists, this book unifies conventional statistical thinking and contemporary machine learning framework into a single overarching umbrella over data science. The book is designed to bridge the knowledge gap between conventional statistics and machine learning. It provides an accessible approach for readers with a basic statistics background to develop a mastery of machine learning. The book starts with elucidating examples in Chapter 1 and fundamentals on refined optimization in Chapter 2, which are followed by common supervised learning methods such as regressions, classification, support vector machines, tree algorithms, and range regressions. After a discussion on unsupervised learning methods, it includes a chapter on unsupervised learning and a chapter on statistical learning with data sequentially or simultaneously from multiple resources.One of the distinct features of this book is the comprehensive coverage of the topics in statistical learning and medical applications. It summarizes the authors' teaching, research, and consulting experience in which they use data analytics. The illustrating examples and accompanying materials heavily emphasize understanding on data analysis, producing accurate interpretations, and discovering hidden assumptions associated with various methods.Key Features:Unifies conventional model-based framework and contemporary data-driven methods into a single overarching umbrella over data science.Includes real-life medical applications in hypertension, stroke, diabetes, thrombolysis, aspirin efficacy.Integrates statistical theory with machine learning algorithms.Includes potential methodological developments in data science. 298 pp. Englisch.

Hardcover. Condition: Brand New. 320 pages. 9.19x6.13x9.21 inches. In Stock. This item is printed on demand.

Hardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. John T. Chen is a professor of Statistics at Bowling Green State University. He completed his postdoctoral training at McMaster University (Canada) after earning a PhD degree in statistics at the University of Sydney (Australia). John has publishe.

Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Written by an experienced statistics educator and two data scientists, this book unifies conventional statistical thinking and contemporary machine learning framework into a single overarching umbrella over data science. The book is designed to bridge the knowledge gap between conventional statistics and machine learning. It provides an accessible approach for readers with a basic statistics background to develop a mastery of machine learning. The book starts with elucidating examples in Chapter 1 and fundamentals on refined optimization in Chapter 2, which are followed by common supervised learning methods such as regressions, classification, support vector machines, tree algorithms, and range regressions. After a discussion on unsupervised learning methods, it includes a chapter on unsupervised learning and a chapter on statistical learning with data sequentially or simultaneously from multiple resources.One of the distinct features of this book is the comprehensive coverage of the topics in statistical learning and medical applications. It summarizes the authors' teaching, research, and consulting experience in which they use data analytics. The illustrating examples and accompanying materials heavily emphasize understanding on data analysis, producing accurate interpretations, and discovering hidden assumptions associated with various methods.Key Features:Unifies conventional model-based framework and contemporary data-driven methods into a single overarching umbrella over data science.Includes real-life medical applications in hypertension, stroke, diabetes, thrombolysis, aspirin efficacy.Integrates statistical theory with machine learning algorithms.Includes potential methodological developments in data science.