Machine Learning: A Practitioner’s Approach

Author:   Vinod Chandra ,  Anand Hareendran, S.
Publisher:   PHI Learning
ISBN:  

9789389347463


Pages:   624
Publication Date:   28 February 2021
Format:   Paperback
Availability:   In Print   Availability explained
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Machine Learning: A Practitioner’s Approach


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Author:   Vinod Chandra ,  Anand Hareendran, S.
Publisher:   PHI Learning
Imprint:   PHI Learning
Weight:   0.795kg
ISBN:  

9789389347463


ISBN 10:   9389347467
Pages:   624
Publication Date:   28 February 2021
Audience:   College/higher education ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

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Vinod Chandra S S, PhD, is Director, Computer Centre, University of Kerala, and the Head of Machine Intelligence Research (MIR) group, a pinpoint research group in machine intelligence and nature-inspired techniques. Dr. Vinod is leading many research and development projects associated with universities and the government and holds many consultancy activities for government organizations. He has five IPRs and a patent in the field of machine learning. An author of six books and a modest number of research publications, he is a reviewer of many international journals and conferences. His research area includes machine intelligence algorithms and nature inspired computing. Anand Hareendran S, PhD, is Associate Professor, Department of Computer Science and Engineering, Muthoot Institute of Technology and Science, Kochi. He has designed and implemented various rule-mining algorithms, which find application in medical field, route mapping and frequent item search. Dr. Anand is an author of two books, a modest number of research journal publications, and has two IPRs in machine learning algorithms. He is a reviewer in various international conferences and journals, and is an active member of Machine Intelligence Research group. His current area of research includes algorithms on machine learning, association rule mining and bio-inspired methodologies.

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