Titlebook: Intelligent Techniques for Data Science; Rajendra Akerkar,Priti Srinivas Sajja Textbook 2016 The Editor(s) (if applicable) and The Author(

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Basic Learning Algorithms,o create value and insight to help organizations to reach new goals. We have seen the term ‘data-driven’ in earlier chapters and have also realized that data is rather useless until we transform it into information. This transformation of data into information is the . for using machine learning.

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Rajendra Akerkar,Priti Srinivas SajjaFocuses 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 machin

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Other Metaheuristics and Classification Approaches,This chapter considers some of the effective metaheuristics and classification techniques that have been applicable in intelligent data analytics. Firstly, metaheuristics approaches such as adaptive memory procedures and swarm intelligence are discussed, and then classification approaches such as case-based reasoning and rough sets are presented.

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d intermediate and advanced techniques.Reinforces the machin.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 f

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Fuzzy Logic,become a valid member of the set. An example of such classical set is the number of students in a class, ‘Student’. Students who have enrolled themselves for the class by paying fees and following rules are the valid members of the class ‘Student’. The class ‘Student’ is crisp, finite and non-negative. Here are some types of crisp sets.