An Introduction to Statistical Learning: with Applications 2nd ed. 2021 Edition

Name: An Introduction to Statistical Learning: with Applications 2nd ed. 2021 Edition
SKU: First0162
Price: 39.99 USD
Availability: InStock
Rating: 4.77 (13 reviews)

Rated 4.77 out of 5 based on 13 customer ratings

(13 customer reviews)

~~$59.00~~ $39.99

Publisher: Springer

Condition: New

ISBN: 978-1071614174

Author: by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

Format: Hardcover

199 in stock

SKU: First0162 Categories: Best Seller, Hardcover, Test Prep & Study Guide Tags: 978-1071614174, Best seller

An Introduction to Statistical Learning offers a clear overview of the area of statistical learning, which is crucial for making sense of the enormous and complicated data sets that have evolved in a variety of fields over the past 20 years, including biology, economics, marketing, and astrophysics. Some of the most significant modelling and prediction strategies are covered in this book, along with pertinent applications. There are several topics covered, including as multiple testing, survival analysis, resampling techniques, shrinkage techniques, support vector machines, clustering, and linear regression. The methodologies are illustrated with colour graphics and actual-world instances. Each chapter of this book includes exercises to help practitioners in science, business, and other sectors more easily employ these statistical learning techniques.

ISBN- 13	978-1071614174
ISBN - 10	1071614177
Condition	New
Publisher	Springer Publishing Company
Format	Hardcover
Edition	2nd
Dimensions	6.5 x 1.25 x 9.5 inches
Item Weight	2.63 pounds
Pages	622

13 reviews for An Introduction to Statistical Learning: with Applications 2nd ed. 2021 Edition

Rated 5 out of 5

Subramanian – June 23, 2023

Every chapter is very well explained and at the end of it there is a lab excercise with R which is very helpful.
Rated 5 out of 5

Marie – June 23, 2023

Machine learning is a form of statistical learning and this book provides a great introduction.
Rated 5 out of 5

Lynn – October 3, 2023

This book was delivered on time, and in new condition. I am satisfied with this purchase and will buy again.
Rated 5 out of 5

NWW – October 4, 2023

Love this book, very glad I purchased it.
Rated 5 out of 5

William Arreaga – April 6, 2024

Every chapter is very well explained and at the end of it there is a lab excercise with R which is very helpful.
Rated 4 out of 5

Ross adjei – April 8, 2024

Every chapter is very well explained and at the end of it there is a lab excercise with R which is very helpful.
Rated 4 out of 5

Nazan sorensen – April 8, 2024

Practical approach to Statistical Learning. Well written by pioneers in the field.
Rated 4 out of 5

Wendy chase – April 8, 2024

I used this book in my statistical learning & data mining course last summer. At the time, the pdf version of this book was available from my university library so I didn’t get the hard copy until now. The reason I decided to get the hard copy is that the theory/conceptual part is well-balanced between proper depth and easy-to-understand. Even though I’m now doing a Machine Learning training program in Python, I still recall the rationale of different models that were well explained in this book. So I’ve decided to get a permanent copy.
Rated 5 out of 5

Lydia foreman – April 10, 2024

Honestly this book carried me through my statistics masters, it had the perfect detail for this course and covered many of my modules
Rated 5 out of 5

Kevin crawford – April 10, 2024

An absolute must read for anyone breaking into machine learning
Rated 5 out of 5

Danice gray – April 18, 2024

Good book for beginners who want to learn about machine learning
Rated 5 out of 5

Bryan olson – April 18, 2024

“This is really a good book. Machine learning is a form of statistical learning and this book provides a great introduction. “
Rated 5 out of 5

Matthew torres – April 29, 2024

Great book and it’s really worth to buy it as it is much more convenient to jump across different section with a book in your hand than with a PDF.