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An Introduction to Statistical Learning

An Introduction to Statistical Learning
  • Author : Gareth James
  • Publsiher : Springer Science & Business Media
  • Release : 24 June 2013
  • ISBN : 1461471389
  • Pages : 426 pages
  • Rating : 5/5 from 1 ratings
GET THIS BOOKAn Introduction to Statistical Learning

Summary:
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.


An Introduction to Statistical Learning

An Introduction to Statistical Learning
  • Author : Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani
  • Publisher : Springer Science & Business Media
  • Release : 24 June 2013
GET THIS BOOKAn Introduction to Statistical Learning

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.

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An Introduction to Statistical Learning

An Introduction to Statistical Learning
  • Author : Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani
  • Publisher : Springer
  • Release : 11 July 2014
GET THIS BOOKAn Introduction to Statistical Learning

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.

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An Introduction to Statistical Learning

An Introduction to Statistical Learning
  • Author : Gareth Michael James,Daniela Witten,Trevor Hastie,Robert Tibshirani
  • Publisher : Springer Nature
  • Release : 27 October 2021
GET THIS BOOKAn Introduction to Statistical Learning

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, deep learning,

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The Elements of Statistical Learning

The Elements of Statistical Learning
  • Author : Trevor Hastie,Robert Tibshirani,Jerome Friedman
  • Publisher : Springer Science & Business Media
  • Release : 11 November 2013
GET THIS BOOKThe Elements of Statistical Learning

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This

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An Elementary Introduction to Statistical Learning Theory

An Elementary Introduction to Statistical Learning Theory
  • Author : Sanjeev Kulkarni,Gilbert Harman
  • Publisher : John Wiley & Sons
  • Release : 09 June 2011
GET THIS BOOKAn Elementary Introduction to Statistical Learning Theory

A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors

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Introduction to Statistical Machine Learning

Introduction to Statistical Machine Learning
  • Author : Masashi Sugiyama
  • Publisher : Morgan Kaufmann
  • Release : 31 October 2015
GET THIS BOOKIntroduction to Statistical Machine Learning

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range

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Statistical Learning with Sparsity

Statistical Learning with Sparsity
  • Author : Trevor Hastie,Robert Tibshirani,Martin Wainwright
  • Publisher : CRC Press
  • Release : 07 May 2015
GET THIS BOOKStatistical Learning with Sparsity

Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data. Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm

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An Introduction to Statistics with Python

An Introduction to Statistics with Python
  • Author : Thomas Haslwanter
  • Publisher : Springer
  • Release : 20 July 2016
GET THIS BOOKAn Introduction to Statistics with Python

This textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics. Working code and data for Python solutions for each test, together with easy-to-follow Python examples, can be reproduced by the reader and reinforce their immediate understanding of the topic. With recent advances in the Python ecosystem, Python has

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A Computational Approach to Statistical Learning

A Computational Approach to Statistical Learning
  • Author : Taylor Arnold,Michael Kane,Bryan W. Lewis
  • Publisher : CRC Press
  • Release : 23 January 2019
GET THIS BOOKA Computational Approach to Statistical Learning

A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset. The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters

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Machine Learning and Data Science

Machine Learning and Data Science
  • Author : Daniel D. Gutierrez
  • Publisher : Technics Publications
  • Release : 01 November 2015
GET THIS BOOKMachine Learning and Data Science

A practitioner’s tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised machine learning and model evaluation. Machine learning and data science are large disciplines, requiring years of study in order to gain proficiency. This book can be viewed as a set

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Causal Inference

Causal Inference
  • Author : Scott Cunningham
  • Publisher : Yale University Press
  • Release : 26 January 2021
GET THIS BOOKCausal Inference

An accessible, contemporary introduction to the methods for determining cause and effect in the social sciences “Causation versus correlation has been the basis of arguments—economic and otherwise—since the beginning of time. Causal Inference: The Mixtape uses legit real-world examples that I found genuinely thought-provoking. It’s rare that a book prompts readers to expand their outlook; this one did for me.”—Marvin Young (Young MC) Causal inference encompasses the tools that allow social scientists to determine what causes

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Statistical Learning from a Regression Perspective

Statistical Learning from a Regression Perspective
  • Author : Richard A. Berk
  • Publisher : Springer Nature
  • Release : 29 June 2020
GET THIS BOOKStatistical Learning from a Regression Perspective

This textbook considers statistical learning applications when interest centers on the conditional distribution of a response variable, given a set of predictors, and in the absence of a credible model that can be specified before the data analysis begins. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis depends in an integrated fashion on sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. The unifying theme is that supervised

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Statistical Learning for Biomedical Data

Statistical Learning for Biomedical Data
  • Author : James D. Malley,Karen G. Malley,Sinisa Pajevic
  • Publisher : Cambridge University Press
  • Release : 24 February 2011
GET THIS BOOKStatistical Learning for Biomedical Data

This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings

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