[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-40470-en":3,"doc-seo-40470-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},40470,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning in Industry","Machine Learning in Industry introduces the fundamentals of machine learning and frames its growing relevance across real-world and daily-life applications. The book highlights how manufacturing and other industrial sectors increasingly deploy ML in plants, especially alongside the Industry 4.0 momentum. The introductory chapter presents core concepts, outlines major classes of ML approaches, and briefly covers both statistical and AI-based techniques including decision trees, linear regression, least squares, artificial neural networks, and clustering methods.","Management and Industrial Engineering  \nShubhabrata Datta  \nJ. Paulo Davim Editors  \nMachine Learning in Industry  \nManagement and Industrial Engineering  \nSeries Editor  \nJ. Paulo Davim, Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal  \nThis series fosters information exchange and discussion on management and industrial engineering and related aspects, namely global management, organizational development and change, strategic management, lean production, performance management, production management, quality engineering, maintenance management, productivity improvement, materials management, human resource management, workforce behavior, innovation and change, technological and organizational flexibility, self-directed work teams, knowledge management, organizational learning, learning organizations, entrepreneurship, sustainable management, etc. The series provides discussion and the exchange of information on principles, strategies, models, techniques, methodologies and applications of management and industrial engineering in the field of the different types of organizational activities. It aims to communicate the latest developments and thinking in what concerns the latest research activity relating to new organizational challenges and changes world-wide. Contributions to this book series are welcome on all subjects related with management and industrial engineering. To submit a proposal or request further information, please contact Professor J. Paulo Davim, Book Series Editor, [pdavim@ua.pt](pdavim@ua.pt)  \nMore information about this series at [http://www.springer.com/series/11690](http://www.springer.com/series/11690)  \nShubhabrata Datta · J. Paulo Davim Editors  \nMachine Learning in Industry  \nEditors  \nShubhabrata Datta   \nDepartment of Mechanical Engineering SRM Institute of Science and Technology Chennai, Tamil Nadu, India  \nJ. Paulo Davim   \nDepartment of Mechanical Engineering University of Aveiro  \nAveiro, Portugal  \nISSN 2365-0532 ISSN 2365-0540 (electronic)  \nManagement and Industrial Engineering  \nISBN 978-3-030-75846-2 ISBN 978-3-030-75847-9 (eBook)  \n[https://doi.org/10.1007/978-3-030-75847-9](https://doi.org/10.1007/978-3-030-75847-9)  \n© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022  \nThis work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.  \nThe use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.  \nThe publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors orthe editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.  \nThis Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland  \nPreface  \nMachine learning (ML) is a method for training computers or making the computer learn automatically from the supplied information or data. Different methods of machine learning originate from the nature and follow the principle ","cbCaivBo0mPSMwjH","https://ap.wps.com/l/cbCaivBo0mPSMwjH","pdf",531539,2,1,21,"English","en",105,"# Preface\n## Machine learning overview\n## ML methods and core techniques\n## Industrial adoption and Industry 4.0","[{\"question\":\"What is the main idea of machine learning described in the book’s preface?\",\"answer\":\"Machine learning is presented as training computers to learn automatically from provided information or data.\"},{\"question\":\"Why does the book emphasize the industrial sector’s adoption of ML?\",\"answer\":\"The preface explains that ML applications in manufacturing and other industrial plants are increasing and becoming effective with Industry 4.0.\"},{\"question\":\"What topics does the introductory chapter cover?\",\"answer\":\"It describes basic concepts, classes of machine learning approaches, and a brief overview of statistical and AI-based techniques such as decision trees, linear regression, least squares, neural networks, and clustering.\"}]",1783311926,53,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-in-industry","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/machine-learning-in-industry/40470/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-10","2026-07-06",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main idea of machine learning described in the book’s preface?","Question",{"text":75,"@type":76},"Machine learning is presented as training computers to learn automatically from provided information or data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why does the book emphasize the industrial sector’s adoption of ML?",{"text":80,"@type":76},"The preface explains that ML applications in manufacturing and other industrial plants are increasing and becoming effective with Industry 4.0.",{"name":82,"@type":73,"acceptedAnswer":83},"What topics does the introductory chapter cover?",{"text":84,"@type":76},"It describes basic concepts, classes of machine learning approaches, and a brief overview of statistical and AI-based techniques such as decision trees, linear regression, least squares, neural networks, and clustering.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]