An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) - Image 1

£30.66

Springer An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

Name: An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
Brand: Springer
Price: 30.66 GBP
Availability: InStock

AI & Machine Learning

It has never been this cheap. We have no record of a lower price.

£31 today · cheaper than every other day in the last 16 months

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 348 days • 348 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 348 days • 4 price ranges

Days at Price

Current Price

Price Analysis

Most common range: £44-48 (271 days, 77.9%)

Price range: £31 - £48

Price levels: 4 price ranges over 348 days

Description

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, survival analysis, multiple testing, 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. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

Key Features

New Store Stock

Product Specifications

Brand: Springer
Format: Paperback
ASIN: 1071614207
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 30 July 2022
Listed Since: 03 July 2022

Barcode

No barcode data available

Springer An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

It has never been this cheap. We have no record of a lower price.

Price History & Forecast

Price Distribution

Price Analysis

Description

Key Features

Product Specifications

Barcode

Similar Products You Might Like

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics)

An Elementary Introduction to Statistical Learning Theory (Wiley Series in Probability and Statistics)

Statistical Learning from a Regression Perspective (Springer Texts in Statistics)

Statistical Learning from a Regression Perspective (Springer Texts in Statistics)

Foundations of Statistics for Data Scientists: With R and Python (Chapman & Hall/CRC Texts in Statistical Science)

Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)

Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver Data Mining

Machine Learning Foundations: Volume 1: Supervised Learning

Machine Learning: Theory and Practice

Deep-Learning-Assisted Statistical Methods with Examples in R (Chapman & Hall/CRC Data Science Series)

Statistical Foundations of Data Science (Chapman & Hall/CRC Data Science Series)

Statistical Learning for Biomedical Data (Practical Guides to Biostatistics and Epidemiology)

Statistical Learning and Data Science (Computer Science and Data Analysis)

A Practical Machine Learning with R: Tutorials and Case Studies

Fundamentals of Supervised Machine Learning: With Applications in Python, R, and Stata (Statistics and Computing)

Mathematics of Machine Learning: Master linear algebra, calculus, and probability for machine learning

Machine Learning – A Probabilistic Perspective

A First Course in Linear Model Theory (Chapman & Hall/CRC Texts in Statistical Science)

Statistical Learning and Data Science (Chapman & Hall/CRC Computer Science & Data Analysis)

Fundamentals of Supervised Machine Learning: With Applications in Python, R, and Stata (Statistics and Computing)

Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner

Model-Based Machine Learning