Página InicialGruposDiscussãoMaisZeitgeist
Pesquisar O Sítio Web
Este sítio web usa «cookies» para fornecer os seus serviços, para melhorar o desempenho, para analítica e (se não estiver autenticado) para publicidade. Ao usar o LibraryThing está a reconhecer que leu e compreende os nossos Termos de Serviço e Política de Privacidade. A sua utilização deste sítio e serviços está sujeita a essas políticas e termos.

Resultados dos Livros Google

Carregue numa fotografia para ir para os Livros Google.

A carregar...

An Introduction to Statistical Learning: with Applications in R

por Gareth James, Trevor Hastie, Robert Tibshirani, Daniela Whitten

MembrosCríticasPopularidadeAvaliação médiaDiscussões
2642100,067 (4.53)Nenhum(a)
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.… (mais)
A carregar...

Adira ao LibraryThing para descobrir se irá gostar deste livro.

Ainda não há conversas na Discussão sobre este livro.

Mostrando 2 de 2
I was lucky to attend a MOOC course delivered by the authors of this book- Trevor Hastie and Robert Tibshirani, which was offered by Stanford University. The book presents a balanced amount of theory and practice of the Statistical (Machine) Learning topic with not-in-depth emphasis on mathematical details. It provides the right amount of know-how for those interested to start working in the Data Science field and in Machine Learning, in specific.

Unlike many other references the book has an edge, especially for Data Scientists using R, of putting every chapter’s concept into R practice through the end-of-chapter R Labs. It is an excellent practical guide to implement Machine Learning especially that it explains the pros and cons of many algorithms used in addition to the emphasis on data processing and cleaning before doing Learning.

The book is a must-read for any one embarking on the journey of Data Science as a profession. ( )
  Mohammedkb | Mar 15, 2016 |
519.5 I61987 2015
  ebr_mills | Mar 23, 2017 |
Mostrando 2 de 2
sem críticas | adicionar uma crítica

» Adicionar outros autores

Nome do autorPapelTipo de autorObra?Estado
Gareth Jamesautor principaltodas as ediçõescalculado
Hastie, Trevorautor principaltodas as ediçõesconfirmado
Tibshirani, Robertautor principaltodas as ediçõesconfirmado
Whitten, Danielaautor principaltodas as ediçõesconfirmado
Tem de autenticar-se para poder editar dados do Conhecimento Comum.
Para mais ajuda veja a página de ajuda do Conhecimento Comum.
Título canónico
Informação do Conhecimento Comum em inglês. Edite para a localizar na sua língua.
Título original
Títulos alternativos
Data da publicação original
Pessoas/Personagens
Locais importantes
Acontecimentos importantes
Filmes relacionados
Epígrafe
Dedicatória
Primeiras palavras
Citações
Últimas palavras
Nota de desambiguação
Editores da Editora
Autores de citações elogiosas (normalmente na contracapa do livro)
Língua original
DDC/MDS canónico
LCC Canónico

Referências a esta obra em recursos externos.

Wikipédia em inglês (1)

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

Não foram encontradas descrições de bibliotecas.

Descrição do livro
Resumo Haiku

Current Discussions

Nenhum(a)

Capas populares

Ligações Rápidas

Avaliação

Média: (4.53)
0.5
1
1.5
2
2.5
3
3.5 1
4 8
4.5
5 11

É você?

Torne-se num Autor LibraryThing.

 

Acerca | Contacto | LibraryThing.com | Privacidade/Termos | Ajuda/Perguntas Frequentes | Blogue | Loja | APIs | TinyCat | Bibliotecas Legadas | Primeiros Críticos | Conhecimento Comum | 203,232,766 livros! | Barra de topo: Sempre visível