{"product_id":"an-introduction-to-statistical-learning-with-applications-in-python-hardcover","title":"An Introduction to Statistical Learning: With Applications in Python - Hardcover","description":"\u003cp\u003eby \u003cb\u003eGareth James\u003c\/b\u003e (Author), \u003cb\u003eDaniela Witten\u003c\/b\u003e (Author), \u003cb\u003eTrevor Hastie\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAn Introduction to Statistical Learning\u003c\/b\u003e 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, marketing, and 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. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. \u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eFour of the authors co-wrote \u003ci\u003eAn Introduction to Statistical Learning, With Applications in R \u003c\/i\u003e(ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAn Introduction to Statistical Learning\u003c\/b\u003e 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, marketing, and 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. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eFour of the authors co-wrote \u003ci\u003eAn Introduction to Statistical Learning, With Applications in R \u003c\/i\u003e(ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eGareth James\u003c\/b\u003e is the John H. Harland Dean of Goizueta Business School at Emory University. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. The conceptual framework for this book grew out of his MBA elective courses in this area.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eDaniela Witten\u003c\/b\u003e is a professor of statistics and biostatistics, and the Dorothy Gilford Endowed Chair, at University of Washington. Her research focuses largely on statistical machine learning techniques for the analysis of complex, messy, and large-scale data, with an emphasis on unsupervised learning.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eTrevor Hastie\u003c\/b\u003e and \u003cb\u003eRobert Tibshirani \u003c\/b\u003eare professors of statistics at Stanford University and are co-authors of the successful textbook \u003ci\u003eElements of Statistical Learning\u003c\/i\u003e. Hastie and Tibshirani developed generalized additive models and wrote a popular book with that title. Hastie co-developed much of the statistical modeling software and environment in R, and invented principal curves and surfaces. Tibshirani invented the lasso and is co-author of the very successful book, \u003ci\u003eAn Introduction to the Bootstrap\u003c\/i\u003e. They are both elected members of the US National Academy of Sciences. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eJonathan Taylor\u003c\/b\u003e is a professor of statistics at Stanford University. His research focuses on selective inference and signal detection in structured noise.\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 60\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.65 x 10.08 x 7.17 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e July 01, 2023\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42703985573951,"sku":"9783031387463","price":233.26,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/d74be24d5448394ec004d25f42001e20.webp?v=1765031587","url":"https:\/\/dhlswag.com\/products\/an-introduction-to-statistical-learning-with-applications-in-python-hardcover","provider":"BBB","version":"1.0","type":"link"}