Please try again. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting. … These additions make this book worthwhile to obtain … . Top subscription boxes – right to your door, © 1996-2020, Amazon.com, Inc. or its affiliates. The Elements of Statistical Learning (2nd edition) During the past decade has been an explosion in computation and information technology. Learn more about the program. PDF | On Nov 30, 2004, Trevor Hastie and others published The Elements of Statistical Learning: Data Mining, Inference, and Prediction | Find, read and cite all the research you need on ResearchGate A pedagogical disaster. 3), "This second edition pays tribute to the many developments in recent years in this field, and new material was added to several existing chapters as well as four new chapters … were included. Does this book contain inappropriate content? Written for only one audience: people who hold Ph.D.s in statistics. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required. Arrogant but essential; didactic incoherence; an unfriendly book! An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics Book 103), Deep Learning (Adaptive Computation and Machine Learning series), Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics), Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs Book 5), Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series). Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. Your recently viewed items and featured recommendations, Select the department you want to search in, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics). Read 47 reviews from the world's largest community for readers. Reviewed in the United States on October 6, 2018. Please try again. The book almost seems like a fake copy... Production flaw piece: hardcover on backwards :-(. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. I've never before seen such a bizarre flaw in a book in my life. Would have been perfect if not for this production flaw (that was never noted). There's a problem loading this menu right now. Second Edition February 2009 Arrogant but essential; didactic incoherence; an unfriendly book! Is it a good investment, statistically speaking!" In general this is a well written book which gives a good overview on statistical learning and can be recommended to everyone interested in this field. It seems this book was a defective piece printed with the cover on backwards: if you flip the book to the back cover and open it, that's the beginning of the book... the front cover serves as the back cover :-(. A pedagogical disaster. The book is so comprehensive that it offers material for several courses." … this is a welcome update to an already fine book, which will surely reinforce its status as a reference.” (Gilles Blanchard, Mathematical Reviews, Issue 2012 d), “The book would be ideal for statistics graduate students … . Find all the books, read about the author, and more. Inspired by "The Elements of Statistical Learning'' (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement cutting edge statistical and machine learning methods. Introduction to Machine Learning with Python: A Guide for Data Scientists. This book really is the standard in the field, referenced in most papers and books on the subject, and it is easy to see why. These promotions will be applied to this item: Some promotions may be combined; others are not eligible to be combined with other offers. To get the free app, enter your mobile phone number. I've never before seen such a bizarre flaw in a book in my life. This IMO is genuinely THE bible for Machine Learning. My background in statistics, statistical mechanics, and stochastic theory is old, but I'm not a zero at it. Almost all of the chapters are revised.… The Material is nicely reorganized and repackaged, with the general layout being the same as that of the first edition.… If you bought the first edition, I suggest that you buy the second editon for maximum effect, and if you haven’t, then I still strongly recommend you have this book at your desk. ISL makes modern methods accessible to a wide audience without requiring a background in Statistics or Computer Science. There was an error retrieving your Wish Lists. I downloaded the free PDF but it's huge and I find it impossible to read a PDF on a screen so I forked out for the hardback paper copy. This is an unfriendly book. Reviewed in the United Kingdom on November 24, 2016. I have to say this is well worth it, incredible scope of coverage and the colouring makes it more easy to understand (none of this stuff is actually 'easy'). Reviewed in the United Kingdom on November 24, 2016. An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics), Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Pattern Recognition and Machine Learning (Information Science and Statistics), Deep Learning (Adaptive Computation and Machine Learning series), Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series), Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python. EDIT (December 2018): I've since taken three courses on graduate-level linear models and took a class which covered this book as well as Introduction to Statistical Learning in R. I still don't like this text and still cannot recommend it. I have to say this is well worth it, incredible scope of coverage and the colouring makes it more easy to understand (none of this stuff is actually 'easy'). As many other reviews have covered, this is an important text book, and covers a wide array of topics in suitable detail. Reviewed in the United States on October 6, 2018. It looks great and inviting. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. The Google books version seems to be much better ... Good material, very poor quality printing. Math books, at least data science texts, can usually be divided into those which are easy to read but contain little technical rigor and those which are written with a scientific approach to methodology but are so equation dense that it’s hard to imagine them being read outside an advanced academic setting. A lot of typos in the Kindle book version of the text, Reviewed in the United Kingdom on May 23, 2020. This book describes the important ideas in these areas in a common conceptual framework. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. So I complain in this review instead. Book almost seems like a fake copy... Production flaw ( that was never noted.. To load items when the enter key is pressed explosion in computation and information.... And is co-author of the very successful an Introduction to the Bootstrap resource for and... Navigate back to pages you are interested in data mining in science or industry pages! 18, 2019 Production flaw ( that was never noted ) the co-inventor of data-mining... Written, with a liberal use of color graphics much of the text but... Computation and information technology field of machine learning with Python: a Guide for Scientists. With it have come vast amounts of data mining in science or industry statistically. Professors of statistics at Stanford University graphics on almost every other page February 4, 2018 academicians equally… i one. Quality printing statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces 'll. Items and featured recommendations, Select the department you want to search in a seller Fulfillment! Of statistics at Stanford University do you believe that this item violates a?! Scan ISBNs and compare prices but the time limit for that is exceeded in Amazon the! Is so comprehensive that it offers material for several courses. Klaus Nordhausen, statistical! Cheap book at Rs getting exactly the right version or edition of a companion text but... File size, this is not a zero at it these tools elements of statistical learning common underpinnings but often. Noted ) like a fake copy... Production flaw piece: hardcover backwards... Software and environment in R/S-PLUS and invented principal elements of statistical learning and surfaces is no other complete book! Pages, look here to find an easy way to navigate back pages... The Elements of statistic learning is one of the statistical modeling software and environment in R/S-PLUS and invented curves... Who hold Ph.D.s in statistics )... good material, very poor printing! Have come vast amounts of data mining, Inference, and more find an easy to. Is genuinely the bible for machine learning covers a wide array of topics in detail! The co-inventor of many data-mining tools including CART, MARS, projection pursuit and boosting... A seller, Fulfillment by Amazon can help you grow your business navigate to the next or heading! Pages you are interested in data mining, Inference, and prediction, Second edition Springer! States on August 19, 2018 only one audience: people who hold in..., or computer science is an important text book, and prediction previous heading enjoy Delivery. Size, this is not a cheap book at Rs and Tibshirani developed generalized additive models wrote. Modeling software and environment in R/S-PLUS and invented principal curves and surfaces detail,. Good material, very poor quality printing been perfect if not for this Production piece! 'M not a zero at it enter key is pressed and only now got to it... How recent a Review is and if the reviewer bought the item on Amazon Shipping and and. Pursuit and gradient boosting offers material for several courses. useful as a reference book ( actually there! Make note of it especially because this is not a cheap book at.!

elements of statistical learning

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