Top 6 Best The Elements Of Statistical Learning

of November 2024
1
Best ChoiceBest Choice
The Elements of Statistical Learning: Data Mining, Inference, and Prediction,
Springer
Springer
10
Exceptional
View on Amazon
2
Best ValueBest Value
Statistical Learning with Math and Python: 100 Exercises for Building Logic
9.9
Exceptional
View on Amazon
3
Elements of Statistical Learning Data Mining, Inference, and Prediction, Second
9.8
Exceptional
View on Amazon
4
An Introduction to Statistical Learning: with Applications in R (Springer Texts
Springer
Springer
9.7
Exceptional
View on Amazon
5
Applied Predictive Modeling
Springer
Springer
9.6
Exceptional
View on Amazon
6
Pattern Recognition and Machine Learning (Information Science and Statistics)
Springer
Springer
9.5
Excellent
View on Amazon
7
Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG,
9.4
Excellent
View on Amazon
8
Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive
The MIT Press
The MIT Press
9.3
Excellent
View on Amazon
9
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable,
9.2
Excellent
View on Amazon
10
Deep Learning (Adaptive Computation and Machine Learning series)
The MIT Press
The MIT Press
9.1
Excellent
View on Amazon

About The Elements Of Statistical Learning

Click here to learn more about these products.

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

Language Published English. Binding Hardcover. Comes in Good condition.

Statistical Learning with Math and Python: 100 Exercises for Building Logic

Elements of Statistical Learning Data Mining, Inference, and Prediction, Second Edition

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

This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, re-sampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, 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.

Applied Predictive Modeling

Springer.

Pattern Recognition and Machine Learning (Information Science and Statistics)

Springer.

Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street

Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

Deep Learning (Adaptive Computation and Machine Learning series)

Language Published English. Binding hardcover. It ensures you get the best usage for a longer period.
Disclaimer