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Intelligent Techniques for Data Science de Rajendra Akerkar

Intelligent Techniques for Data Science

Autor Rajendra Akerkar, Priti Srinivas Sajja
en Limba Engleză Hardback – 18 oct 2016
This textbook provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods. These embrace the family of neural networks, fuzzy systems and evolutionary computing in addition to other fields within machine learning, and will help in identifying, visualizing, classifying and analyzing data to support business decisions./p>
The authors, discuss advantages and drawbacks of different approaches, and present a sound foundation for the reader to design and implement data analytic solutions for real‐world applications in an intelligent manner. Intelligent Techniques for Data Science also provides real-world cases of extracting value from data in various domains such as retail, health, aviation, telecommunication and tourism.
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Specificații

ISBN-13: 9783319292052
ISBN-10: 3319292056
Pagini: 340
Ilustrații: XVI, 272 p. 121 illus., 57 illus. in color.
Dimensiuni: 155 x 235 x 24 mm
Greutate: 0.58 kg
Ediția:1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland

Cuprins

Preface.- Introduction.- Data Analytics.- Basic Learning Algorithms.- Fuzzy Logic.- Artificial Neural Networks.- Genetic Algorithms and Evolutionary Computing.- Other Metaheuristics and Classification Approaches.- Analytics and Big Data.- Data Analytics Using R.- Appendix I: Tools for Data Science.- Appendix II: Tools for Computational Intelligence.

Notă biografică

Rajendra Akerkar is a professor of information technology at Western Norway Research Institute, Norway. He has 23 years of research and teaching experience in artificial intelligent systems, semantic technologies and big data science. His recent research focuses on real world use of big data, and social media analysis in a wide set of semantic dimensions. He has held senior positions in the key academic conference committees, journal boards and review committees in those fields and he has supervised Ph.D. and research M.Sc. projects in intelligent systems, web intelligence and data science.  He has managed 12 international ICT initiatives, and data-intensive research & development projects for more than 17 years.
Dr Priti Srinivas Sajja (b.1970) joined the faculty of the Department of Computer Science, Sardar Patel University, India in 1994 and is presently working as a Professor. She received her M.S. (1993) and Ph.D (2000) in Computer Science from the Sardar Patel University. Her research interests include knowledge-based systems, soft computing, multi-agent systems, and software engineering. She has 152 publications in books, book chapters, journals, and in the proceedings of national and international conferences out of which five publications have won best research paper awards. She is co-author of 'Knowledge-Based Systems' and 'Intelligent Technologies for Web Applications' published in the USA. She is supervising work of a few doctoral research scholars while six candidates have completed their Ph.D research under her guidance. She was Principal Investigator of a major research project funded by UGC, India. She is serving as a member on the editorial board of many international science journals and served as a program committee member for various international conferences.  
 

Textul de pe ultima copertă

This textbook provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods. These embrace the family of neural networks, fuzzy systems and evolutionary computing in addition to other fields within machine learning, and will help in identifying, visualizing, classifying and analyzing data to support business decisions.
The authors, discuss advantages and drawbacks of different approaches, and present a sound foundation for the reader to design and implement data analytic solutions for applications in an intelligent manner. Intelligent Techniques for Data Science also provides real-world cases of extracting value from data in various domains such as retail, health, aviation, telecommunication and tourism.

Caracteristici

Focuses on methods significantly beneficial in data science, and clearly describes them at an introductory level, with extensions to selected intermediate and advanced techniques Reinforces the machine learning principles with necessary demonstrations in the field of data science Integrates illustrations, cases and examples to support pedagogical exposition Equips readers with the necessary information to obtain hands-on experience of data science