{"product_id":"an-introduction-to-statistical-learning-with-applications-in-r-2nd-edition-springer-texts-in-statistics","title":"An Introduction to Statistical Learning with Applications in R, 2nd Edition - Springer Texts in Statistics","description":"\u003cp\u003e\u003cstrong\u003ePublisher:\u003c\/strong\u003e Springer\u003cbr\u003e\n\u003cstrong\u003eCondition:\u003c\/strong\u003e New\u003cbr\u003e\n\u003cstrong\u003eISBN-13:\u003c\/strong\u003e 9781071614174\u003cbr\u003e\n\u003cstrong\u003eAuthor:\u003c\/strong\u003e Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani\u003cbr\u003e\n\u003cstrong\u003eFormat:\u003c\/strong\u003e Hardcover\u003c\/p\u003e\n\n\u003cp\u003eThe second edition of An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie and Tibshirani, published in hardcover by Springer in the Springer Texts in Statistics series.\u003c\/p\u003e\n\n\u003cp\u003eThe book presents the main supervised and unsupervised learning methods with the mathematics kept to what a reader with a basic statistics background can follow: linear and logistic regression, resampling and model selection, tree-based methods, support vector machines, unsupervised learning, and in this edition additional material including deep learning and survival analysis. Each chapter ends with R labs.\u003c\/p\u003e\n\n\u003cp\u003eUsed as the entry text for statistical learning and applied machine learning courses, and by analysts and data scientists teaching themselves the field.\u003c\/p\u003e","brand":"Binge pages","offers":[{"title":"Default Title","offer_id":49280862322827,"sku":null,"price":49.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0760\/6786\/4715\/files\/9781071614174_7812d5ae-3bca-4813-a8cb-79a06b5c9c14.webp?v=1788219947","url":"https:\/\/bingepages.com\/products\/an-introduction-to-statistical-learning-with-applications-in-r-2nd-edition-springer-texts-in-statistics","provider":"Binge Pages","version":"1.0","type":"link"}