000			02691 a2200337 4500
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008			260214b |||||||| |||| 00| 0 eng d
020			_a9783031391897
082			_a519.5 JAM
100			_aJames, Gareth
245			_aIntroduction to statistical learning: with applications in Python
260			_aSwitzerland: _bSpringer, _c2023.
300			_axv, 607p.: _bcol., ill.; pbk.: _c25 cm.
440			_aSpringer Texts in Statistics
504			_aIncludes Index.
520			_aAn 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, 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. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R(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. https://link.springer.com/book/10.1007/978-3-031-38747-0
650			_aStatistical Learning
650			_aMachine Learning
650			_aData Mining
650			_aRegression Analysis
650			_aBig Data—Statistical Methods
650			_aResampling Methods
650			_aShrinkage Approaches
650			_aSupport Vector Machines
650			_aDeep Learning
700			_a Witten, Daniela _eCo-author
700			_aHastie, Trevor _eCo-author
700			_aTibshirani, Robert _eCo-author
700			_aTaylor, Jonathan _eCo-author
942			_cTD _2ddc
999			_c64223 _d64223