Paperback. Machine Learning is an integral tool in a business analysts 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 is an integral tool in a business analysts arsenal because the rate at which data is being generated from different sources is increasing and working on complex unstructured data is becoming inevitable. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo: Machine Learning for Business Analytics (Paperback)
Editor: Taylor & Francis Ltd, London
Fecha de publicaci�n: 2022
Idioma: Ingl�s
ISBN 10: 1032072776
ISBN 13: 9781032072777
Encuadernaci�n: Paperback
Estado: new

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Sinopsis

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.

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Dr. Hemachadran K completed B.Tech in Electronics and Communication engineering from Dr.MGR Educational & Research Institute University, India in 2007 as well as M.Tech in VLSI & Embedded Systems and Ph.D. in Interdisciplinary of ECE / EEE in 2011 and 2017, respectively. Most of his publications are Scopus / SCI indexed. He has guided more than 50 M.Tech &B.Tech projects. He served in the Advisory board to many National and International Conferences and is serving as an Editor and reviewer to many reputed journals.

Dr. Sayantan Khanra pursued Ph.D. in Strategic Management from the Indian Institute of Management Rohtak. He is a visiting research scholar at the National Taiwan University of Science and Technology and the Turku School of Economics, Finland. His research interests relate to the strategic analysis of various components of a digital economy. Some of his research is presented at prestigious conferences organized by the Academy of Management, Academy of International Business, Pan-IIM Committee, and UNESCO, among others. His research papers are published in quality international journals, such as Enterprise Information Systems, Journal of Hospitality and Tourism Management, and Tourism Management Perspectives

Dr. Raul V. Rodriguez holds an MBA, MHRM, and MSc in Big Data and BI from Universidad Isabel I, Spain and has completed his Ph.D. in Artificial Intelligence and Robotic Process Applications to HR from San Miguel University, Mexico.

His specific areas of expertise and interest are Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Robotic Process Automation, Multi-agent Systems, Knowledge Engineering, and Quantum Artificial Intelligence. He is adept in the latest programming languages & software such as Prolog, Java, JavaScript, C++, Python, R/RStudio, Julia, Swift, Scala, MySQL, Tableau, Spark, among others.

A registered expert in Artificial intelligence, Intelligent Systems, and Multi-agent Systems at the European Commission, Dr. Raul has been nominated for the Forbes 30 Under 30 Europe 2020 list, and awardee at the 40 Under 40 Europe India Leaders. Alongside this, he is a regular keynote speaker and panel moderator at various national and international conferences or summits. Additionally, he is a member of the Harvard Business Review Advisory Council, the Oxford Artificial Intelligence Society, part of the University of Oxford, and the Institute for Robotics Process Automation & Artificial Intelligence.

Dr. Juan R. Jaramillo is an associate professor and the director of the Master of Science in Business Analytics in the Robert Willumstad School of Business at Adelphi University. He holds a Ph.D. in Industrial Engineering from West Virginia University. His published research spans the fields of Analytics, Logistics, Operations Management, and Health Care Analytics. Juan has been an invited editor of the INFORMS Journal on Applied Analytics and the Journal of Modelling in Management. Juan has been the chair and co-chair of the INFORMS Innovative Applications in Analytics Award besides being a judge of the award since its inception. He is the inaugural recipient of the prestigious Michael F. Gorman award for his contribution to the Analytics Society of INFORMS.

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