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An Introduction to Statistical Learning with Applications in R 2nd Edition book (9781071614174) is a systematic and user-friendly study book for anybody who wishes to learn statistical modeling and machine learning methods with the help of the R language. Well received by students, educators, and experts, the book presents a practical study resource for learning important statistical procedures that play a pivotal role in data science, analytics, and applied studies.

This 2nd edition of An Introduction to Statistical Learning with Applications in R has been revised comprehensively so as to encompass new examples, new data sets, and new approaches. With a discussion of important concepts like linear regression, classification, resampling techniques, tree-based techniques, support vector machines, and unsupervised learning, the book combines theory and coding in R in an interactive manner so as to make learning interesting and rewarding.

Every chapter in the An Introduction to Statistical Learning book (9781071614174) features applied labs and R code examples so readers can apply techniques in real-time. The book also features fresh content on deep learning, survival analysis, and multiple testing, and as such, now presents itself as being more relevant than ever to modern data science problems.

Written for readers with minimal background in mathematics, the An Introduction to Statistical Learning 2nd Edition Book brings complicated ideas into reach without losing rigor. You may be a student starting out in statistical learning or a professional seeking to add a new tool to your analytical arsenal. Either way, this book provides the right blend of clarity, depth, and application.

This edition (9781071614174) as a reliable scholarly and professional resource endorsed by leading institutions and data science educators globally. Want practical know-how in R and statistical learning? The An Introduction to Statistical Learning with Applications in R 2nd Edition Book is your trusted companion to learning the fundamentals and beyond.