Machine Learning For Business Analytics
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Machine Learning For Business Analytics
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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2023-05-09
Machine Learning For Business Analytics written by Galit Shmueli and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-09 with Mathematics categories.
MACHINE LEARNING FOR BUSINESS ANALYTICS An up-to-date introduction to a market-leading platform for data analysis and machine learning Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed. offers an accessible and engaging introduction to machine learning. It provides concrete examples and case studies to educate new users and deepen existing users’ understanding of their data and their business. Fully updated to incorporate new topics and instructional material, this remains the only comprehensive introduction to this crucial set of analytical tools specifically tailored to the needs of businesses. Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed. readers will also find: Updated material which improves the book’s usefulness as a reference for professionals beyond the classroom Four new chapters, covering topics including Text Mining and Responsible Data Science An updated companion website with data sets and other instructor resources: www.jmp.com/dataminingbook A guide to JMP Pro's new features and enhanced functionality Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed. is ideal for students and instructors of business analytics and data mining classes, as well as data science practitioners and professionals in data-driven industries.
Machine Learning For Business Analytics
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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2023-04-19
Machine Learning For Business Analytics written by Galit Shmueli and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-19 with Computers categories.
MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning—also known as data mining or predictive 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. Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver® Data Mining provides a comprehensive introduction and an overview of this methodology. The fourth edition of this best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, time series forecasting 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. This fourth edition of Machine Learning for Business Analytics also includes: An expanded chapter on deep learning A new chapter on experimental feedback techniques, including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates 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 A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This 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.
Machine Learning For Business Analytics
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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2023-03-02
Machine Learning For Business Analytics written by Galit Shmueli and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-02 with Computers categories.
Machine Learning for Business Analytics Machine 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. Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner 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. This is the seventh edition of Machine Learning for Business Analytics, and the first using RapidMiner software. This edition also includes: A new co-author, Amit Deokar, who brings experience teaching business analytics courses using RapidMiner Integrated use of RapidMiner, an open-source machine learning platform that has become commercially popular in recent years An expanded chapter focused on discussion of deep learning techniques A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates 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 A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This 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.
Machine Learning For Business Analytics
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Author : Hemachandran K
language : en
Publisher: CRC Press
Release Date : 2022-07-21
Machine Learning For Business Analytics written by Hemachandran K and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-21 with Business & Economics categories.
Machine Learning is an integral tool in a business analyst’s arsenal because the rate at which data is being generated from different sources is increasing and working on complex unstructured data is becoming inevitable. Data collection, data cleaning, and data mining are rapidly becoming more difficult to analyze than just importing information from a primary or secondary source. The machine learning model plays a crucial role in predicting the future performance and results of a company. In real-time, data collection and data wrangling are the important steps in deploying the models. Analytics is a tool for visualizing and steering data and statistics. Business analysts can work with different datasets -- choosing an appropriate machine learning model results in accurate analyzing, forecasting the future, and making informed decisions. The global machine learning market was valued at $1.58 billion in 2017 and is expected to reach $20.83 billion in 2024 -- growing at a CAGR of 44.06% between 2017 and 2024. The authors have compiled important knowledge on machine learning real-time applications in business analytics. This book enables readers to get broad knowledge in the field of machine learning models and to carry out their future research work. The future trends of machine learning for business analytics are explained with real case studies. Essentially, this book acts as a guide to all business analysts. The authors blend the basics of data analytics and machine learning and extend its application to business analytics. This book acts as a superb introduction and covers the applications and implications of machine learning. The authors provide first-hand experience of the applications of machine learning for business analytics in the section on real-time analysis. Case studies put the theory into practice so that you may receive hands-on experience with machine learning and data analytics. This book is a valuable source for practitioners, industrialists, technologists, and researchers.
Machine Learning For Business Analytics
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Author : Hemachandran K
language : en
Publisher: CRC Press
Release Date : 2022-07-21
Machine Learning For Business Analytics written by Hemachandran K and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-21 with Business & Economics categories.
Machine Learning is an integral tool in a business analyst’s arsenal because the rate at which data is being generated from different sources is increasing and working on complex unstructured data is becoming inevitable. Data collection, data cleaning, and data mining are rapidly becoming more difficult to analyze than just importing information from a primary or secondary source. The machine learning model plays a crucial role in predicting the future performance and results of a company. In real-time, data collection and data wrangling are the important steps in deploying the models. Analytics is a tool for visualizing and steering data and statistics. Business analysts can work with different datasets -- choosing an appropriate machine learning model results in accurate analyzing, forecasting the future, and making informed decisions. The global machine learning market was valued at $1.58 billion in 2017 and is expected to reach $20.83 billion in 2024 -- growing at a CAGR of 44.06% between 2017 and 2024. The authors have compiled important knowledge on machine learning real-time applications in business analytics. This book enables readers to get broad knowledge in the field of machine learning models and to carry out their future research work. The future trends of machine learning for business analytics are explained with real case studies. Essentially, this book acts as a guide to all business analysts. The authors blend the basics of data analytics and machine learning and extend its application to business analytics. This book acts as a superb introduction and covers the applications and implications of machine learning. The authors provide first-hand experience of the applications of machine learning for business analytics in the section on real-time analysis. Case studies put the theory into practice so that you may receive hands-on experience with machine learning and data analytics. This book is a valuable source for practitioners, industrialists, technologists, and researchers.
Foundations Of Programming Statistics And Machine Learning For Business Analytics
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Author : Ram Gopal
language : en
Publisher: SAGE
Release Date : 2023-04-22
Foundations Of Programming Statistics And Machine Learning For Business Analytics written by Ram Gopal and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-22 with Business & Economics categories.
This book provides an introduction to the key concepts in programming, statistics and machine learning needed by business analytics students, assuming no prior knowledge and taking a step-by-step approach to help students build up their confidence.
Ai And Business Analytics Leveraging Machine Learning For Enhanced Business Intelligence
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Author : SHACHI GHANSHYAM SAYATA, DR. ABHISHEK SINGH VERMA
language : en
Publisher: DeepMisti Publication
Release Date : 2025-01-22
Ai And Business Analytics Leveraging Machine Learning For Enhanced Business Intelligence written by SHACHI GHANSHYAM SAYATA, DR. ABHISHEK SINGH VERMA and has been published by DeepMisti Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-22 with Computers categories.
In the ever-evolving world of business, data has emerged as one of the most valuable assets. With businesses generating an immense amount of data every day, the challenge lies in turning this data into actionable insights that drive informed decision-making. The convergence of Artificial Intelligence (AI) and business analytics provides a transformative approach to harnessing the potential of data, making it possible for organizations to gain deeper insights, enhance operational efficiency, and innovate in their strategies. "AI and Business Analytics: Leveraging Machine Learning for Enhanced Business Intelligence" explores this powerful intersection, offering a comprehensive understanding of how machine learning (ML) techniques are being utilized to revolutionize the business landscape. The book, authored by Shachi Ghanshyam Sayata and Dr. Abhishek Singh Verma, delves into the theoretical underpinnings of AI and ML while providing practical applications for businesses striving to gain a competitive edge in an increasingly data-driven world. The authors bring their expertise to the forefront by discussing a wide array of topics, from the basics of machine learning algorithms to their advanced applications in real-world business scenarios. The integration of AI in business intelligence allows companies to make predictions, optimize processes, and personalize customer experiences with unprecedented accuracy. As the digital transformation accelerates, businesses are adopting AI-driven analytics to stay ahead of market trends, anticipate challenges, and uncover new opportunities. This book aims to serve as a valuable resource for professionals, researchers, and students alike. It offers a balanced mix of theoretical concepts and hands-on applications, ensuring readers not only grasp the fundamental principles of AI and ML but also learn how to implement these techniques in business environments effectively. It is particularly relevant for those seeking to understand the practical integration of these technologies into business operations, and how AI can lead to smarter decision-making processes that ultimately drive growth and success. In summary, "AI and Business Analytics" provides the necessary tools and insights for leveraging machine learning technologies to unlock the full potential of business intelligence. The authors’ collective expertise and practical approach make this book an indispensable guide for anyone looking to explore the dynamic fusion of AI, business analytics, and machine learning in today’s rapidly changing business world. We hope that this work will inspire both the academic and professional community to engage with these exciting technologies, thereby contributing to the next generation of AI-powered business innovations. Authors
Artificial Intelligence For Business Analytics
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Author : Felix Weber
language : en
Publisher: Springer Nature
Release Date : 2023-03-01
Artificial Intelligence For Business Analytics written by Felix Weber and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-01 with Computers categories.
While methods of artificial intelligence (AI) were until a few years ago exclusively a topic of scientific discussions, today they are increasingly finding their way into products of everyday life. At the same time, the amount of data produced and available is growing due to increasing digitalization, the integration of digital measurement and control systems, and automatic exchange between devices (Internet of Things). In the future, the use of business intelligence (BI) and a look into the past will no longer be sufficient for most companies.Instead, business analytics, i.e., predictive and predictive analyses and automated decisions, will be needed to stay competitive in the future. The use of growing amounts of data is a significant challenge and one of the most important areas of data analysis is represented by artificial intelligence methods.This book provides a concise introduction to the essential aspects of using artificial intelligence methods for business analytics, presents machine learning and the most important algorithms in a comprehensible form using the business analytics technology framework, and shows application scenarios from various industries. In addition, it provides the Business Analytics Model for Artificial Intelligence, a reference procedure model for structuring BA and AI projects in the company. This book is a translation of the original German 1st edition Künstliche Intelligenz für Business Analytics by Felix Weber, published by Springer Fachmedien Wiesbaden GmbH, part of Springer Nature in 2020. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.
Advances In Artificial Business Analytics And Quantum Machine Learning
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Author : KC Santosh
language : en
Publisher: Springer Nature
Release Date : 2024-09-18
Advances In Artificial Business Analytics And Quantum Machine Learning written by KC Santosh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-18 with Computers categories.
This book presents select proceedings of the 3rd International Conference on “Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects” (Com-IT-Con 2023) held at the Manav Rachna University in July 2023. It covers topics such as artificial intelligence and business analytics, virtual/augmented reality, quantum information systems, cyber security, data science, and machine learning. The book is useful for researchers and professionals interested in the broad field of communication engineering.
Machine Learning Techniques For Improved Business Analytics
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Author : G., Dileep Kumar
language : en
Publisher: IGI Global
Release Date : 2018-07-06
Machine Learning Techniques For Improved Business Analytics written by G., Dileep Kumar and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-06 with Business & Economics categories.
Analytical tools and algorithms are essential in business data and information systems. Efficient economic and financial forecasting in machine learning techniques increases gains while reducing risks. Providing research on predictive models with high accuracy, stability, and ease of interpretation is important in improving data preparation, analysis, and implementation processes in business organizations. Machine Learning Techniques for Improved Business Analytics is a collection of innovative research on the methods and applications of artificial intelligence in strategic business decisions and management. Featuring coverage on a broad range of topics such as data mining, portfolio optimization, and social network analysis, this book is ideally designed for business managers and practitioners, upper-level business students, and researchers seeking current research on large-scale information control and evaluation technologies that exceed the functionality of conventional data processing techniques.