Machine Learning for Decision Sciences with Case Studies in Python (PDF)
This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a...
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This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data.
Features:
- Explains the basic concepts of Python and its role in machine learning.
- Provides comprehensive coverage of feature engineering including real-time case studies.
- Perceives the structural patterns with reference to data science and statistics and analytics.
- Includes machine learning-based structured exercises.
- Appreciates different algorithmic concepts of machine learning including unsupervised, supervised, and reinforcement learning.
This book is aimed at researchers, professionals, and graduate students in data science, machine learning, computer science, and electrical and computer engineering.
Dr. S. Sumathi is working as a Professor in the Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore with teaching and research experience of 30 years. Her research interests include Neural Networks, Fuzzy Systems and Genetic Algorithms, Pattern Recognition and Classification, Data Warehousing and Data Mining, Operating systems and Parallel Computing. She is the author of more than 40 papers in refereed journals and international conferences. She has authored books with reputed publishers such as Springer and CRC Press.
Dr. L. Ashok Kumar was a Postdoctoral Research Fellow from San Diego State University, California. He is a recipient of the BHAVAN fellowship from the Indo-US Science and Technology Forum and SYST Fellowship from DST, Govt. of India. His current research focuses on integration of Renewable Energy Systems in the Smart Grid and Wearable Electronics. He has 3 years of industrial experience and 19 years of academic and research experience. He has published 167 technical papers in International and National journals and presented 157 papers in National and International Conferences. He has authored 10 books with leading publishers like CRC, Springer and Elsevier. He has completed 26 Government of India funded projects, and currently 7 projects are in progress.
Dr. Suresh Rajappa PhD PMP MBA is seasoned senior IT management consulting professional with 25 years' experience leading large global IT programs and projects in IT Strategy, Finance IT (FINTECH) Transformation Strategy, BI and data warehousing / Data Analytics and Management for multiple fortune 100 clients across diverse industries, generating millions of dollars to top and bottom lines. Successful recruiting and leading onshore/offshore cross-cultural teams to deliver complex enterprise-wide solutions within tight deadlines and budgets. Highly effective at breaking down strategic
Dr. Surekha Paneerselvam is an Assistant Professor (Sr. Gr) in the Department of Electrical and Electronics Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India with 20 years of experience in teaching, industry and research. She has published 35 papers in International and National journals and conferences. She has authored 7 books with leading publishers such as CRC Press and Springer. Her research interests include Control Systems, Computational Intelligence, Machine Learning, Signal and Image Processing, Embedded Systems, Real time operating systems, and Virtual Instrumentation.
- Autoren: S. Sumathi , Suresh Rajappa , L Ashok Kumar , Surekha Paneerselvam
- 2022, 1. Auflage, 476 Seiten, Englisch
- Verlag: Taylor & Francis
- ISBN-10: 1000590933
- ISBN-13: 9781000590937
- Erscheinungsdatum: 08.07.2022
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Größe: 17 MB
- Ohne Kopierschutz
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