Designate: $20.90
| Situation: |
Mark Unique: A brand recent, unread, unused book in ideal situation with out a missing or broken pages. Belief the vendor’s |
Format: | Hardcover |
| Language: | English | E-newsletter three hundred and sixty five days: | 2017 |
| ISBN: |
9781461471370 |
| Product Data | |
| An Introduction to Statistical Learning presents an accessible overview of the sphere of statistical discovering out, an significant toolset for making sense of the wide and complex recordsdata sets that have emerged in fields ranging from biology to finance to advertising and marketing to astrophysics prior to now twenty years. This book presents a pair of of the finest modeling and prediction techniques, alongside with connected applications. Subject matters embody linear regression, classification, resampling ideas, shrinkage approaches, tree-based fully ideas, toughen vector machines, clustering, and more. Color graphics and proper-world examples are used as an instance the ideas presented. For the reason that aim of this textbook is to facilitate the usage of these statistical discovering out techniques by practitioners in science, industry, and other fields, each and every chapter contains an tutorial on imposing the analyses and ideas presented in R, a in particular standard originate provide statistical utility platform.Two of the authors co-wrote The Parts of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a most traditional reference book for statistics and machine discovering out researchers. An Introduction to Statistical Learning covers most of the same issues, but at a level accessible to an most most necessary wider target audience. This book is centered at statisticians and non-statisticians alike who would in point of fact like to make expend of lowering-edge statistical discovering out techniques to analyze their recordsdata. The text assumes fully a outdated course in linear regression and no recordsdata of matrix algebra. | |
| Product Identifiers | |
| Creator | Springer |
| ISBN-10 | 1461471370 |
| ISBN-13 | 9781461471370 |
| eBay Product ID (ePID) | 159944459 |
| Product Key Parts | |
| Format | Hardcover |
| E-newsletter three hundred and sixty five days | 2017 |
| Language | English |
| Dimensions | |
| Weight | 30.1 Oz.19459027] |
| Width | 6.1in. |
| Length | 9.3in. |
| Further Product Parts | |
| Sequence of Volumes | 1 Vol. |
| Dewey Version | 23 |
| Sequence Volume Amount | 103 |
| Illustrated | Walk |
| Dewey Decimal | 519.5 |
| Sequence | Springer Texts in Statistics |
| Copyright Date | 2013 |
| Author | Daniela Witten, Gareth James, Robert Tibshirani, Trevor Hastie |
| Sequence of Pages | 426 Pages |
| Lc Classification Amount | Qa276-280qa276-280q3 |
| E-newsletter Date | 2017-09-01 |
| Critiques | “An Introduction to Statistical Learning (ISL)” by James, Witten, Hastie and Tibshirani is the “how to” manual for statistical discovering out. Inspired by “The Parts of Statistical Learning” (Hastie, Tibshirani and Friedman), this book presents obvious and intuitive steering on how to place in power lowering edge statistical and machine discovering out ideas. ISL makes fresh ideas accessible to a huge target audience without requiring a background in Statistics or Computer Science. The authors give proper, wise explanations of what ideas are readily accessible, and when to make expend of them, including say R code. Any individual who wants to intelligently analyze complex recordsdata have to serene non-public this book. Larry Wasserman , Professor, Division of Statistics and Machine Learning Division, Carnegie Mellon University, “…Apart from the apparent skills of the authors on this field, every other cause why the aim of the book is reached so successfully is the structure of each and every chapter. A detailed lab section follows on the discontinuance of each and every chapter which illustrates the utility to instance recordsdata sets in R accompanied by the annotated R code. The chapters conclude with conceptual and applied workout routines. All recordsdata used on this book are either already in R or are supplied in an R equipment accompanying the book and the code from the lab sessions is additionally readily accessible on the book’s Online page…These two books [‘An Introduction to Statistical Learning’ and ‘The Elements of Statistical Learning’] will flow very well together, in particular when instructing these how to undergraduate college students in statistics or laptop science or to varsity students from applied fields.” Global Statistical Review (2014), 82, 1, overview by Klaus Nordhausen “An Introduction to Statistical Learning (ISL)” by James, Witten, Hastie and Tibshirani is the “how to” manual for statistical discovering out. Inspired by “The Parts of Statistical Learning” (Hastie, Tibshirani and Friedman), this book presents obvious and intuitive steering on how to place in power lowering edge statistical and machine discovering out ideas. ISL makes fresh ideas accessible to a huge target audience without requiring a background in Statistics or Computer Science. The authors give proper, wise explanations of what ideas are readily accessible, and when to make expend of them, including say R code. Any individual who wants to intelligently analyze complex recordsdata have to serene non-public this book. Larry Wasserman , Professor, Division of Statistics and Machine Learning Division, Carnegie Mellon University, From the reviews: “The book excels in providing the theoretical and mathematical basis for machine discovering out, and now at prolonged closing, a intellectual uncover with the inclusion of R programming examples. It is some distance the latter portion of the update that I have been looking ahead to because it proper now applies to my work in recordsdata science. Give the recent affirm of this book, I’d classify it as the authoritative text for any machine discovering out practitioner…Here is one book you wish to internet have to you furthermore mght can very well be thinking about this rising field.” (Daniel Gutierrez, Inner Immense Data, inner-bigdata.com, October 2013) “The acknowledged cause of this book is to facilitate the transition of statistical discovering out to mainstream. … it adds recordsdata by including more detail and R code to seemingly the most issues in Parts of Statistical Learning. … I am having rather a entire lot of enjoyable taking part in with the code that goes with book. I am elated that this used to be written.” (Mary Anne, Cats and Canine with Data, maryannedata.com, June, 2014) “It objectives to introduce fresh statistical discovering out how to varsity students, researchers and practitioners who’re basically attracted to analysing recordsdata and are making an try to be confined fully with the implementation of the statistical methodology and subsequent interpretation of the outcomes. … the book additionally demonstrates how to expend these ideas the usage of more than a few R applications by providing detailed worked examples the usage of attention-grabbing proper recordsdata applications.” (Klaus Nordhausen, Global Statistical Review, Vol. 82 (1), 2014) “The book is structured in ten chapters masking tools for modeling and mining of complex proper existence recordsdata sets. … The vogue is well excellent for undergraduates and researchers … and the idea of ideas is facilitated by the workout routines, each and every wise and theoretical, which accompany every chapter.” (Irina Ioana Mohorianu, zbMATH, Vol. 1281, 2014), From the reviews: “It objectives to introduce fresh statistical discovering out how to varsity students, researchers and practitioners who’re basically attracted to analysing recordsdata and are making an try to be confined fully with the implementation of the statistical methodology and subsequent interpretation of the outcomes. … the book additionally demonstrates how to expend these ideas the usage of more than a few R applications by providing detailed worked examples the usage of attention-grabbing proper recordsdata applications.” (Klaus Nordhausen, Global Statistical Review, Vol. 82 (1), 2014) “The book is structured in ten chapters masking tools for modeling and mining of complex proper existence recordsdata sets. … The vogue is well excellent for undergraduates and researchers … and the idea of ideas is facilitated by the workout routines, each and every wise and theoretical, which accompany every chapter.” (Irina Ioana Mohorianu, zbMATH, Vol. 1281, 2014), From the book reviews: “This book has a in fact strong relief that sets it well prior to the opponents by plot of discovering out about machine discovering out: it covers the full most most necessary minute print that one has to know in elaborate to expend or put in power a machine discovering out algorithm in a proper-world insist. Hence, this book will no doubt be of hobby to readers from many fields, ranging from laptop science to industry administration and advertising and marketing.” (Charalambos Poullis, Computing Critiques, September, 2014) “The book presents a correct introduction to R. The code to your entire statistical ideas launched in the book is fastidiously defined. … the book will no doubt be purposeful to many of us (including me). I will completely expend many examples, labs and datasets from this book in my very non-public lectures.” (Pierre Alquier, Mathematical Critiques, July, 2014) “The acknowledged cause of this book is to facilitate the transition of statistical discovering out to mainstream. … it adds recordsdata by including more detail and R code to seemingly the most issues in Parts of Statistical Learning. … I am having rather a entire lot of enjoyable taking part in with the code that goes with book. I am elated that this used to be written.” (Mary Anne, Cats and Canine with Data, maryannedata.com, June, 2014) “This book (ISL) is a unswerving Master’s level introduction to statistical discovering out: statistics for complex datasets. … the homework problems in ISL are at a Master’s level for faculty students who’re making an try to learn to make expend of statistical discovering out how to analyze recordsdata. … ISL contains 12 very treasured R labs that blow their non-public horns how to make expend of most of the statistical discovering out ideas with the R equipment ISLR … .” (David Olive, Technometrics, Vol. 56 (2), May presumably, 2014) “It objectives to introduce fresh statistical discovering out how to varsity students, researchers and practitioners who’re basically attracted to analysing recordsdata and are making an try to be confined fully with the implementation of the statistical methodology and subsequent interpretation of the outcomes. … the book additionally demonstrates how to expend these ideas the usage of more than a few R applications by providing detailed worked examples the usage of attention-grabbing proper recordsdata applications.” (Klaus Nordhausen, Global Statistical Review, Vol. 82 (1), 2014) “The book is structured in ten chapters masking tools for modeling and mining of complex proper existence recordsdata sets. … The vogue is well excellent for undergraduates and researchers … and the idea of ideas is facilitated by the workout routines, each and every wise and theoretical, which accompany every chapter.” (Irina Ioana Mohorianu, zbMATH, Vol. 1281, 2014) “The book excels in providing the theoretical and mathematical basis for machine discovering out, and now at prolonged closing, a intellectual uncover with the inclusion of R programming examples. It is some distance the latter portion of the update that I have been looking ahead to because it proper now applies to my work in recordsdata science. Give the recent affirm of this book, I’d classify it as the authoritative text for any machine discovering out practitioner…Here is one book you wish to internet have to you furthermore mght can very well be thinking about this rising field.” (Daniel Gutierrez, Inner Immense Data, inner-bigdata.com, October 2013), From the book reviews: “The acknowledged cause of this book is to facilitate the transition of statistical discovering out to mainstream. … it adds recordsdata by including more detail and R code to seemingly the most issues in Parts of Statistical Learning. … I am having rather a entire lot of enjoyable taking part in with the code that goes with book. I am elated that this used to be written.” (Mary Anne, Cats and Canine with Data, maryannedata.com, June, 2014) “This book (ISL) is a unswerving Master’s level introduction to statistical discovering out: statistics for complex datasets. … the homework problems in ISL are at a Master’s level for faculty students who’re making an try to learn to make expend of statistical discovering out how to analyze recordsdata. … ISL contains 12 very treasured R labs that blow their non-public horns how to make expend of most of the statistical discovering out ideas with the R equipment ISLR … .” (David Olive, Technometrics, Vol. 56 (2), May presumably, 2014) “It objectives to introduce fresh statistical discovering out how to varsity students, researchers and practitioners who’re basically attracted to analysing recordsdata and are making an try to be confined fully with the implementation of the statistical methodology and subsequent interpretation of the outcomes. … the book additionally demonstrates how to expend these ideas the usage of more than a few R applications by providing detailed worked examples the usage of attention-grabbing proper recordsdata applications.” (Klaus Nordhausen, Global Statistical Review, Vol. 82 (1), 2014) “The book is structured in ten chapters masking tools for modeling and mining of complex proper existence recordsdata sets. … The vogue is well excellent for undergraduates and researchers … and the idea of ideas is facilitated by the workout routines, each and every wise and theoretical, which accompany every chapter.” (Irina Ioana Mohorianu, zbMATH, Vol. 1281, 2014) “The book excels in providing the theoretical and mathematical basis for machine discovering out, and now at prolonged closing, a intellectual uncover with the inclusion of R programming examples. It is some distance the latter portion of the update that I have been looking ahead to because it proper now applies to my work in recordsdata science. Give the recent affirm of this book, I’d classify it as the authoritative text for any machine discovering out practitioner…Here is one book you wish to internet have to you furthermore mght can very well be thinking about this rising field.” (Daniel Gutierrez, Inner Immense Data, inner-bigdata.com, October 2013) |
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