While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry.
The many topics include neural networks, support vector machines, classification trees and boostingthe first comprehensive treatment of this topic in any book. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap.
Friedman is the co-inven. Score: 5. 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 , 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. Virtually all of the testable terms, concepts, persons, places, and events from the textbook are included.
Cram Just the FACTS studyguides give all of the outlines, highlights, notes, and quizzes for your textbook with optional online comprehensive practice tests. Only Cram is Textbook Specific. Accompanys: Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more.
This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion.
R code has been updated throughout to ensure compatibility. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title.
Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Step-1 : Read the Book Name and author Name thoroughly.
Step-4 : Click the Download link provided below to save your material in your local drive. LearnEngineering team try to Helping the students and others who cannot afford buying books is our aim.
For any quarries, Disclaimer are requested to kindly contact us , We assured you we will do our best. Thank you. If you face above Download Link error try this Link.
Other Useful Links.
0コメント