04024nam 2200625 450 991045339530332120220223214314.01-68015-358-71-78216-215-1(CKB)2550000001138614(OCoLC)862380117(CaPaEBR)ebrary10794279(SSID)ssj0001139763(PQKBManifestationID)11649255(PQKBTitleCode)TC0001139763(PQKBWorkID)11220486(PQKB)11737178(MiAaPQ)EBC1343653(CaSebORM)9781782162148(PPN)227990579(Au-PeEL)EBL1343653(CaPaEBR)ebr10794279(CaONFJC)MIL538284(EXLCZ)99255000000113861420111102d2013 uy 0engurcnu||||||||txtccrMachine learning with R /Brett Lantz1st editionBirmingham :Packt Publishing,2013.1 online resource (396 p.)Community experience distilledIncludes index.1-78216-214-3 1-306-07033-3 R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required – this book will take you methodically through every stage of applying machine learning. Harness the power of R for statistical computing and data science Use R to apply common machine learning algorithms with real-world applications Prepare, examine, and visualize data for analysis Understand how to choose between machine learning models Packed with clear instructions to explore, forecast, and classify data In Detail Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data. "Machine Learning with R" is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions. How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process. We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data. "Machine Learning with R" will provide you with the analytical tools you need to quickly gain insight from complex data.Community experience distilled.Machine learningStatistical methodsHandbooks, manuals, etcR (Computer program language)Handbooks, manuals, etcProgramming languages (Electronic computers)Electronic books.Machine learningStatistical methodsR (Computer program language)Programming languages (Electronic computers)Lantz Brett858284MiAaPQMiAaPQMiAaPQBOOK9910453395303321Machine learning with R1916294UNINA03050nam 2200589 a 450 991077864860332120230421033240.00-309-17460-01-282-08217-597866120821770-309-51825-30-585-02427-8(CKB)110986584752548(EBL)3375542(SSID)ssj0000104913(PQKBManifestationID)11653216(PQKBTitleCode)TC0000104913(PQKBWorkID)10100370(PQKB)11766157(MiAaPQ)EBC3375542(Au-PeEL)EBL3375542(CaPaEBR)ebr10040959(OCoLC)817957828(EXLCZ)9911098658475254819970709d1997 uy 0engur|n|---|||||txtccrApproaching death[electronic resource] improving care at the end of life /Committee on Care at the End of Life, Division of Health Care Services, Institute of Medicine ; Marilyn J. Field and Christine K. Cassel, editorsWashington, D.C. National Academy Press19971 online resource (455 p.)Description based upon print version of record.0-309-09002-4 0-309-06372-8 Includes bibliographical references and index.Front Matter; Preface; Acknowledgments; Contents; TABLES, FIGURES, AND BOXES; Summary; 1 Introduction; 2 A Profile of Death and Dying in America; 3 Caring at the End of Life; 4 The Health Care System and the Dying Patient; 5 Accountability and Quality in End-of-Life Care; 6 Financial and Economic Issues in End-of-Life Care; 7 Legal Issues; 8 Educating Clinicians and Other Professionals; 9 Directions for Research to Improve Care at the End of Life; 10 Conclusions and Recommendations; References; A Institute of Medicine Feasibility Study on Care at the End of Life1 August 1993-February 1994B Institute of Medicine Committee on Care at the End of Life Public MeetingsC Examples of Initiatives to Improve Care at the End of Life*; D Prognosis and Clinical Predictive Models for Critically Ill Patients; E Cultural Diversity in Decisionmaking About Care at the End of Life; F Measuring Care at the End of Life; G Excerpts from Medical Guidelines for Determining Prognosis in Selected NonCancer Diseases*; H American Board of Internal Medicine Clinical Competence in End-of-Life Care*; I Examples of Medical Education Curricula; J Committee Biographies; IndexTerminal careUnited StatesTerminal care362.1/75/0973Field Marilyn J(Marilyn Jane)1477341Cassel Christine K1516545Institute of Medicine (U.S.).Committee on Care at the End of Life.MiAaPQMiAaPQMiAaPQBOOK9910778648603321Approaching death3753093UNINA