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Fundamentals of Machine Learning - Mia Williams - Bog

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The scientific study of statistical models and algorithms that computer systems use in order to perform a specific task without any explicit instructions is referred to as machine learning. It relies on patterns and inference. Machine learning is a subset of artificial intelligence. The study of mathematical optimization contributes significantly to the methods, applications and theory of machine learning. Some of the different models, which are used within this field are artificial neural networks, decision trees and Bayesian networks. Machine learning is applied in various other fields such as in machine perception, agriculture, adaptive websites, bioinformatics, optimization, sentiment analysis, etc. The topics included in this book on machine learning are of utmost significance and bound to provide incredible insights to readers. It unfolds the innovative aspects of this field, which will be crucial for the progress of this field in the future. Those in search of information to further their knowledge will be greatly assisted by this book.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9781647280284
  • Indbinding:
  • Hardback
  • Sideantal:
  • 208
  • Udgivet:
  • 1. marts 2022
  • Størrelse:
  • 203x276x13 mm.
  • Vægt:
  • 726 g.
  • 2-3 uger.
  • 16. december 2024
Forlænget returret til d. 31. januar 2025

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Beskrivelse af Fundamentals of Machine Learning

The scientific study of statistical models and algorithms that computer systems use in order to perform a specific task without any explicit instructions is referred to as machine learning. It relies on patterns and inference. Machine learning is a subset of artificial intelligence. The study of mathematical optimization contributes significantly to the methods, applications and theory of machine learning. Some of the different models, which are used within this field are artificial neural networks, decision trees and Bayesian networks. Machine learning is applied in various other fields such as in machine perception, agriculture, adaptive websites, bioinformatics, optimization, sentiment analysis, etc. The topics included in this book on machine learning are of utmost significance and bound to provide incredible insights to readers. It unfolds the innovative aspects of this field, which will be crucial for the progress of this field in the future. Those in search of information to further their knowledge will be greatly assisted by this book.

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