Titlebook: Data Analytics; Models and Algorithm Thomas A. Runkler Textbook 20203rd edition Springer Fachmedien Wiesbaden GmbH, part of Springer Nature

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BAIL

Suman Singh,Aniruddha Das,Amaresh C. Pandailtering methods with different effectiveness and computational complexities: moving statistical measures, discrete linear filters, finite impulse response, infinite impulse response. Data features with different ranges often need to be standardized or transformed.

Debrief

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https://doi.org/10.1007/978-981-13-1426-1bilities and limitations are presented in detail: the naive Bayes classifier, linear discriminant analysis, the support vector machine (SVM) using the kernel trick, nearest neighbor classifiers, learning vector quantification, and hierarchical classification using decision trees.

instill

Textbook 20203rd editionvantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. This book has been used for more than ten years in the Data Mining course at the Technical University of Munich. Much of the content is based on the re

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