S. Sumathi, Suresh Rajappa, L Ashok Kumar, Surekha Paneerselvam

Machine Learning for Decision Sciences with Case Studies in Python (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book provides a detailed description of machine learning algorithms in Data Analytics, Data Science Lifecycle, Python for Machine Learning, Linear Regression, Logistic Regression and so forth. The focus is on Python programming for machine learning and patterns involved in decision science for handling data including real-world examples.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 476
• Erscheinungstermin: 8. Juli 2022
• Englisch
• ISBN-13: 9781000590937
• Artikelnr.: 64070600

Autorenporträt

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 program/project initiatives into tactical plans and processes to achieve aggressive customer goals. Excel at leveraging strategic partnerships, global resources, process improvements, and best practices to maximize project delivery performance and ROI. Inspirational, solution-focused leader with exceptional ability managing multimillion-dollar P&Ls/budgets and change management initiatives. As an adjunct professor, Dr. Suresh Rajappa teaches data science for graduate and doctoral students at PSG College of technology. His Industry specializations include Utility, Finance (Banking and Insurance) and HiTech Manufacturing. He is a frequent speaker Microsoft PASS conferences, SAP Financials and SAP TechEd conferences on Data Analytics related topics. He also teaches Data Analytics and IT Project Management for Undergraduate and Graduate level students. He is also key note speaker in International Conference on Artificial Intelligence, Smart Grid and Smart City Applications.

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.

Inhaltsangabe

1. Introduction 2. Overview of Python for Machine Learning 3. Data Analytics Life Cycle for Machine Learning 4. Unsupervised Learning 5. Supervised Learning: Regression 6. Supervised Learning: Classification 7. Feature Engineering 8. Reinforcement Learning 9. Case Studies for Decision Sciences Using Python

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1. Introduction 2. Overview of Python for Machine Learning 3. Data
Analytics Life Cycle for Machine Learning 4. Unsupervised Learning 5.
Supervised Learning: Regression 6. Supervised Learning: Classification 7.
Feature Engineering 8. Reinforcement Learning 9. Case Studies for Decision
Sciences Using Python