An Introduction to Statistical Learning Book [PDF] Download

Download the fantastic book titled An Introduction to Statistical Learning written by Gareth James, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "An Introduction to Statistical Learning", which was released on 01 August 2023. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Mathematics genre.

Summary of An Introduction to Statistical Learning by Gareth James PDF

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, marketing, and 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, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.


Detail About An Introduction to Statistical Learning PDF

  • Author : Gareth James
  • Publisher : Springer Nature
  • Genre : Mathematics
  • Total Pages : 617 pages
  • ISBN : 3031387473
  • PDF File Size : 37,8 Mb
  • Language : English
  • Rating : 4/5 from 21 reviews

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An Introduction to Statistical Learning

An Introduction to Statistical Learning
  • Publisher : Springer Nature
  • File Size : 34,8 Mb
  • Release Date : 01 August 2023
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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

Statistics for Machine Learning

Statistics for Machine Learning
  • Publisher : Packt Publishing Ltd
  • File Size : 42,9 Mb
  • Release Date : 21 July 2017
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Build Machine Learning models with a sound statistical understanding. About This Book Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically

Statistical Machine Learning

Statistical Machine Learning
  • Publisher : CRC Press
  • File Size : 41,7 Mb
  • Release Date : 24 June 2020
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The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical

Python for Probability, Statistics, and Machine Learning

Python for Probability, Statistics, and Machine Learning
  • Publisher : Springer
  • File Size : 51,8 Mb
  • Release Date : 29 June 2019
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This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical

Probability for Statistics and Machine Learning

Probability for Statistics and Machine Learning
  • Publisher : Springer Science & Business Media
  • File Size : 53,7 Mb
  • Release Date : 17 May 2011
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This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools

Introduction to Statistical Machine Learning

Introduction to Statistical Machine Learning
  • Publisher : Morgan Kaufmann
  • File Size : 38,8 Mb
  • Release Date : 31 October 2015
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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

Data Science and Machine Learning

Data Science and Machine Learning
  • Publisher : CRC Press
  • File Size : 21,9 Mb
  • Release Date : 20 November 2019
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Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

All of Statistics

All of Statistics
  • Publisher : Springer Science & Business Media
  • File Size : 31,9 Mb
  • Release Date : 11 December 2013
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Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a

Statistics with Julia

Statistics with Julia
  • Publisher : Springer Nature
  • File Size : 51,7 Mb
  • Release Date : 04 September 2021
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This monograph uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data

Statistics for Machine Learning

Statistics for Machine Learning
  • Publisher : BPB Publications
  • File Size : 31,6 Mb
  • Release Date : 15 January 2021
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A practical guide that will help you understand the Statistical Foundations of any Machine Learning Problem Ê KEY FEATURESÊ _ Develop a Conceptual and Mathematical understanding of Statistics _ Get an overview of