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LEADER 00000cam a2200661 a 4500
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006 m o d
007 cr cnu---unuuu
008 220528s2022 xx o 000 0 eng d
020 9781000615425
020 1000615421
020 9781003206316|q(electronic bk.)
020 100320631X|q(electronic bk.)
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020 9781000615449|q(electronic bk.)
024 7 10.4324/9781003206316|2doi
029 1 AU@|b000072966693
035 (OCoLC)1321788331
037 9781003206316|bTaylor & Francis
037 9781000615449|bO'Reilly Media
040 EBLCP|beng|epn|cEBLCP|dTYFRS|dEBLCP|dTYFRS|dOCLCF|dOCLCQ
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049 INap
082 04 658.05631
082 04 658.05631|223/eng/20220608
099 eBook O'Reilly for Public Libraries
100 1 K, Hemachandran.
245 10 Machine Learning for Business Analytics :|bReal-Time Data
Analysis for Decision-Making.|h[O'Reilly electronic
resource]
260 Milton :|bProductivity Press,|c2022.
300 1 online resource (191 pages)
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
520 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 billionin 2017 and is expected to reach $20.83
billionin 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 thetheory 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.
545 0 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.
588 0 Print version record.
590 O'Reilly|bO'Reilly Online Learning: Academic/Public
Library Edition
650 0 Machine learning.
650 0 Business|xData processing.
650 6 Apprentissage automatique.
650 6 Gestion|xInformatique.
650 7 Business|xData processing|2fast
650 7 Machine learning|2fast
700 1 Khanra, Sayantan.
700 1 Rodriguez, Raul V.
700 1 Jaramillo, Juan.
776 08 |iPrint version:|aK, Hemachandran.|tMachine Learning for
Business Analytics.|dMilton : Productivity Press, ©2022
|z9781032072814
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
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