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Understanding Machine Learning

Om Understanding Machine Learning

Machine learning is a branch of computer science concerned with the application of statistical techniques to improve performance of computer systems in the execution of specific tasks. It is significantly used when designing and programming algorithms for operation of computing tasks become ineffective, especially in the detection of network intruders, email filtering, etc. Some of the primary approaches to machine learning include decision tree learning, artificial neural network learning algorithm, deep learning and association rule learning, besides others. Applications of machine learning can extend into the domains of agriculture, bioinformatics, linguistics, marketing, economics, etc. This book elucidates the concepts and innovative models around prospective developments with respect to the field of machine learning. It aims to serve as a resource guide for students and experts alike and contribute to the growth of the discipline.

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  • Språk:
  • Engelsk
  • ISBN:
  • 9781632409119
  • Bindende:
  • Hardback
  • Sider:
  • 270
  • Utgitt:
  • 18. juni 2019
  • Dimensjoner:
  • 254x178x16 mm.
  • Vekt:
  • 689 g.
  Gratis frakt
Leveringstid: 2-4 uker
Forventet levering: 25. mai 2026

Beskrivelse av Understanding Machine Learning

Machine learning is a branch of computer science concerned with the application of statistical techniques to improve performance of computer systems in the execution of specific tasks. It is significantly used when designing and programming algorithms for operation of computing tasks become ineffective, especially in the detection of network intruders, email filtering, etc. Some of the primary approaches to machine learning include decision tree learning, artificial neural network learning algorithm, deep learning and association rule learning, besides others. Applications of machine learning can extend into the domains of agriculture, bioinformatics, linguistics, marketing, economics, etc. This book elucidates the concepts and innovative models around prospective developments with respect to the field of machine learning. It aims to serve as a resource guide for students and experts alike and contribute to the growth of the discipline.

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