An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) 2nd Edition by Gareth James | (PDF) Free Download

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, deep learning, survival analysis, multiple testing, 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.This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐Good book for beginners who want to learn about machine learning

⭐This is a good book as an introduction to Statistical Learning even for a Japanese, who is not good at English.

⭐I recently used this book along with a couple others in a graduate level ML course. IMO it was the best in terms of striking a good balance; containing enough detail to help grasp theory but not so much that it becomes a slog to get through. I used the Ebook in class and liked it enough to buy a hardcopy. Unfortunately, the print font size is quite small. Overall dimension is smaller than listed on Amazon. Maybe that was how big the 1st edition was?

⭐The book immediately hits the reader with terminology and hieroglyphics that’s incomprehensible to those that are new at programming. It uses R. I chose Python.

⭐This book is an amazing resource to get your understanding across many different methods in line. One of the greatest tools of a data scientist and statistician in general is knowledge of best method, or best tool, for a task. Many solutions in data science right now go far too heavily toward one size fits all and this books helps one understand why knowing how to read your results and why to use the method to solve it really, really matter.

⭐It wasn’t was new as I expected. I is in good shape but there is clearly scoff black marks had a sticker over the ISBN label and that sticker was faded. It will work but I would have sold as “good”

⭐Now it includes neural networks and survival analysis.

⭐This book went above my understanding. I found it to be beyond an introduction to statistics.

⭐If you know a little statistics and basics of using R / RStudio then this book will be very useful.Bought it for a Masters course but provides a lot of background for business analytics as well

⭐Honestly this book carried me through my statistics masters, it had the perfect detail for this course and covered many of my modules

⭐This is really a good book. Machine learning is a form of statistical learning and this book provides a great introduction.

⭐I reviewed this book for a class in my master’s program and I loved it from start to end.I already knew most of the concepts but became hooked because of how clear the explanations are. The authors convey complex ideas with remarkable simplicity, and for that, I think this is the most important book for data scientists.I am an avid opposer of the R programming language (ew) and even I enjoyed the applied programming parts of the book.In all honesty, the applications in R are very good, but it’s not the main focus of the book. I think people should read this to understand the inner workings of the most popular AI algorithms instead of learning how to train predictive models (especially in R, haha).Overall, I think this is a great book for beginners and veterans alike. I would not hesitate to recommend this book to anyone interested in statistics, data and AI.

⭐This is a great book. I minus one star since I received a damaged copy. This book has a plastic wrap, hence it is less damaged than another book shipped in the same order.

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