|
Product 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, 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.
Features
- 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
Customers Who Bought This Item Also Bought
- Deep Learning (Adaptive Computation and Machine Learning series)
- Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition
- Pattern Recognition and Machine Learning (Information Science and Statistics)
- Data Mining for Business Analytics: Concepts, Techniques, and Applications in R
- Practical Statistics for Data Scientists: 50 Essential Concepts
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
- Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
- Applied Predictive Modeling
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
*If this is not the "An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)" product you were looking for, you can check the other results by clicking this link. Details were last updated on Nov 3, 2024 21:04 +08.