Machine learning with R

• Verfasst von: Lantz, Brett [VerfasserIn]
• Titel: Machine learning with R
• Titelzusatz: learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data
• Verf.angabe: Brett Lantz
• Ausgabe: Fourth edition.
• Verlagsort: Birmingham, UK
• Verlag: Packt Publishing Ltd.
• Jahr: 2023
• Umfang: 1 online resource (762 pages)
• Illustrationen: illustrations
• Gesamttitel/Reihe: Expert insight
• Fussnoten: Includes index
• ISBN: 978-1-80107-605-0
• ISBN: 1-80107-605-7
• ISBN: 978-1-80107-132-1
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe: Lantz, Brett: Machine Learning with R. - Fourth edition. - Birmingham : Packt, 2023. - xxi, 737 Seiten
• Sach-SW: Business applications
• Sach-SW: COM094000
• Sach-SW: COMPUTERS / Enterprise Applications
• Sach-SW: Machine learning
• Sach-SW: Maschinelles Lernen
• Sach-SW: Unternehmensanwendungen
• K10plus-PPN: 1850984492
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4343652289
• Lokale URL Inst.: Zum Volltext

Abstract

Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Fourth Edition, provides a hands-on, accessible, and readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to know for data pre-processing, uncovering key insights, making new predictions, and visualizing your findings. This 10th Anniversary Edition features several new chapters that reflect the progress of machine learning in the last few years and help you build your data science skills and tackle more challenging problems, including making successful machine learning models and advanced data preparation, building better learners, and making use of big data. You'll also find this classic R data science book updated to R 4.0.0 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Whether you're looking to take your first steps with R for machine learning or making sure your skills and knowledge are up to date, this is an unmissable read that will help you find powerful new insights in your data.