國立虎尾科技大學 |

Intelligent techniques for data science

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Intelligent techniques for data science/ by Rajendra Akerkar, Priti Srinivas Sajja.
作者:	Akerkar, Rajendra.
其他作者:	Sajja, Priti Srinivas.
出版者:	Cham :Springer International Publishing : : 2016.,
面頁冊數:	xvi, 272 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Quantitative research. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-29206-9
ISBN:	9783319292069

Intelligent techniques for data science
Akerkar, Rajendra.

Intelligent techniques for data science[electronic resource] /by Rajendra Akerkar, Priti Srinivas Sajja. - Cham :Springer International Publishing :2016. - xvi, 272 p. :ill. (some col.), digital ;24 cm.

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.

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.

ISBN: 9783319292069

Standard No.: 10.1007/978-3-319-29206-9doiSubjects--Topical Terms:

635913
Quantitative research.

LC Class. No.: Q180.55.Q36

Dewey Class. No.: 001.42

Intelligent techniques for data science
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505 0 $a 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.
520 $a 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.
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