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\u003cp\u003e\u003cb\u003eMACHINE LEARNING \u003csmall\u003eFOR\u003c/small\u003e BUSINESS ANALYTICS\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eMachine learning --also known as data mining or data analytics-- is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003ci\u003eMachine Learning for Business Analytics: Concepts, Techniques, and Applications in R\u003c/i\u003e provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques.\u003c/p\u003e \u003cp\u003eThis is the second R edition of \u003ci\u003eMachine Learning for Business Analytics\u003c/i\u003e. This edition also includes:\u003c/p\u003e \u003cul\u003e \u003cli\u003eA new co-author, Peter Gedeck, who brings over 20 years of experience in machine learning using R\u003c/li\u003e \u003cli\u003eAn expanded chapter focused on discussion of deep learning techniques\u003c/li\u003e \u003cli\u003eA new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning\u003c/li\u003e \u003cli\u003eA new chapter on responsible data science\u003c/li\u003e \u003cli\u003eUpdates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students\u003c/li\u003e \u003cli\u003eA full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques\u003c/li\u003e \u003cli\u003eEnd-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented\u003c/li\u003e \u003cli\u003eA companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions\u003c/li\u003e \u003c/ul\u003e \u003cp\u003eThis textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.\u003c/p\u003e |
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