Title Dēta anaritikusu no tame no kikai gakushū nyūmon : arugorizumu, jitsurei, kēsu sutadi / cho J.D. Kerahā, B. Makunamī, A. Dāshī ; yaku Miyaoka Etsuo, Shimokawa Asanao, Kurosawa Takuma = Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies / J.D. Kelleher, B. Mac Namee, A. D'Arcy データアナリティクスのための機械学習入門 : アルゴリズム・実例・ケーススタディ / 著J.D. ケラハー, B. マクナミー, A. ダーシー ; 訳宮岡悦良, 下川朝有, 黒沢匠雅 = Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies / J.D. Kelleher, B. Mac Namee, A. D'Arcy

Edition Shohan 初版 Published Tōkyō-to Shinjuku-ku : Kindai Kagakusha, 2022 東京都新宿区 : 近代科学社, 2022

Click on the following:
EBSCO eBooks

Copies

Description 1 online resource (xvi, 454 pages). : illustrations Series Sekai hyōjun MIT kyōkasho 世界標準MIT教科書 Sekai hyōjun MIT kyōkasho 世界標準MIT教科書 Summary "Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals"--Provided by publisher Bibliography Includes bibliographical references (pages 438-447) and index Notes Online resource; title from PDF title page (EBSCO, viewed October 17, 2022) Subject Machine learning. Data mining. Prediction theory. Data mining Machine learning Prediction theory 880-05 Kikai gakushū. 880-05/$1 機械学習. Form Electronic book Author Mac Namee, Brian, author D'Arcy, Aoife, 1978- author. Miyaoka, Etsuo, translator 宮岡悦良, translator ISBN 9784764972902 4764972905

Other Titles Fundamentals of machine learning for predictive data analytics. Japanese Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies

  Permalink