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

£77.28

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

Name: An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics)
Brand: Springer
Price: 77.28 GBP
Availability: InStock

Languages

About as cheap as it gets. The only time it was cheaper was 2 months ago.

£77 today · all-time low £76 (Mar 2026) · usually the usual

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 7 days • 7 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 7 days • 3 price levels

Days at Price

Current Price

Price Analysis

Most common price: £76 (3 days, 42.9%)

Price range: £76 - £79

Price levels: 3 different prices over 7 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, 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.

Product Specifications

Brand: Springer
Format: hardcover
ASIN: 3031387465
Category: Books > Subjects > Computing & Internet > Programming > Languages
Domain: Amazon UK
Release Date: 01 July 2023
Listed Since: 17 June 2023

Barcode

No barcode data available

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

About as cheap as it gets. The only time it was cheaper was 2 months ago.

Price History & Forecast

Price Distribution

Price Analysis

Description

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 R (Springer Texts in Statistics)

Applied Machine Learning Using mlr3 in R

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

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

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)

Applied Statistics with Python: Two-Volume Set

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

Machine Learning: Theory and Practice

Machine Learning with R

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Machine Learning Foundations: Volume 1: Supervised Learning

Practical Business Analytics Using R and Python: Solve Business Problems Using a Data-driven Approach

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

Practical Machine Learning for Data Analysis Using Python

Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R

Flexible Regression and Smoothing: Using GAMLSS in R (Chapman & Hall/CRC The R Series)

No-Code Data Science: Mastering Advanced Analytics, Machine Learning, and Artificial Intelligence

Recent Trends in Learning From Data: Tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019): 896 (Studies in Computational Intelligence, 896)

Applied Machine Learning for Data Science Practitioners

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

Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems: 1

Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems: 1