An introduction to statistical learning audiobook

AN INTRODUCTION TO STATISTICAL LEARNING AUDIOBOOK HOW TO
AN INTRODUCTION TO STATISTICAL LEARNING AUDIOBOOK SOFTWARE

An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. 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 AUDIOBOOK SOFTWARE

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. Color graphics and real-world examples are used to illustrate the methods presented. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. This book presents some of the most important modeling and prediction techniques, along with relevant applications. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.ДЄuthor: Gareth Michael James,Daniela Witten,Trevor Hastie,Robert TibshiraniДЄn 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 is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data.

Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Publisher: Springer Science & Business MediaДЄn 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. Distributed storage and parallel computing need be considered to avoid loss of data and to make computations efficient.ДЎ:16 Skip to 1 minute and 16 seconds Join us and cope with big data using R and RHadoop.Author: Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani These issues necessarily involve the use of high performance computers.

AN INTRODUCTION TO STATISTICAL LEARNING AUDIOBOOK HOW TO

Large-scale scientific instruments, social network platforms, cloud solutions, digital cultural heritage are only a few examples of sources of huge amount of text, photo, video and audio materials which are considered big data.Д :55 Skip to 0 minutes and 55 seconds But questions related to data have not changed much: how to store and maintain it, how to understand and how to learn from the data for an improved response in the future. Luckily, more and more data is digital, but expressed in different formats. Yet it is only today that we have to deal with really big data. Books, documents, drawings, maps and paintings are examples of such data. 0:25 Skip to 0 minutes and 25 seconds Nearly every historical period may be said to have had sources of data that were considered big for that time.

An introduction to statistical learning audiobook