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.
An Introduction to Statistical Learning: with Applications 2nd ed. 2021 Edition
$79.99
Springer
New
978-1071614174
by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Hardcover
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|---|---|
| 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
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Subramanian –
Every chapter is very well explained and at the end of it there is a lab excercise with R which is very helpful.
Marie –
Machine learning is a form of statistical learning and this book provides a great introduction.
Lynn –
This book was delivered on time, and in new condition. I am satisfied with this purchase and will buy again.
NWW –
Love this book, very glad I purchased it.
William Arreaga –
Every chapter is very well explained and at the end of it there is a lab excercise with R which is very helpful.
Ross adjei –
Every chapter is very well explained and at the end of it there is a lab excercise with R which is very helpful.
Nazan sorensen –
Practical approach to Statistical Learning. Well written by pioneers in the field.
Wendy chase –
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.
Lydia foreman –
Honestly this book carried me through my statistics masters, it had the perfect detail for this course and covered many of my modules
Kevin crawford –
An absolute must read for anyone breaking into machine learning
Danice gray –
Good book for beginners who want to learn about machine learning
Bryan olson –
“This is really a good book. Machine learning is a form of statistical learning and this book provides a great introduction. “
Matthew torres –
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.