Introduction to Algorithms for Data Mining and Machine Learning

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data. Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages

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  • Author : Xin-She Yang
  • Publisher : Academic Press
  • Pages : 188 pages
  • ISBN : 0128172169
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKIntroduction to Algorithms for Data Mining and Machine Learning

Introduction to Algorithms for Data Mining and Machine Learning

Introduction to Algorithms for Data Mining and Machine Learning
  • Author : Xin-She Yang
  • Publisher : Academic Press
  • Release : 15 July 2019
GET THIS BOOKIntroduction to Algorithms for Data Mining and Machine Learning

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but

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  • Publisher : Horwood Publishing
  • Release : 14 May 2007
GET THIS BOOKMachine Learning and Data Mining

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  • Publisher : Addison-Wesley
  • Release : 13 April 2021
GET THIS BOOKIntroduction to Data Mining

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  • Publisher : Morgan Kaufmann
  • Release : 01 October 2016
GET THIS BOOKData Mining

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  • Publisher : Springer
  • Release : 12 October 2006
GET THIS BOOKIntroduction to Data Mining and its Applications

This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in database systems, and presents a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization.

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  • Publisher : CRC Press
  • Release : 29 March 2012
GET THIS BOOKAdvances in Machine Learning and Data Mining for Astronomy

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  • Publisher : Univ of California Press
  • Release : 01 May 2015
GET THIS BOOKData Mining for the Social Sciences

"We live, today, in world of big data. The amount of information collected on human behavior every day is staggering, and exponentially greater than at any time in the past. At the same time, we are inundated by stories of powerful algorithms capable of churning through this sea of data and uncovering patterns. These techniques go by many names - data mining, predictive analytics, machine learning - and they are being used by governments as they spy on citizens and

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  • Publisher : MIT Press
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The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in

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  • Release : 16 March 2018
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  • Publisher : CRC Press
  • Release : 12 July 2017
GET THIS BOOKStatistical and Machine-Learning Data Mining:

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Statistical and Machine-Learning Data Mining

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  • Publisher : CRC Press
  • Release : 28 February 2012
GET THIS BOOKStatistical and Machine-Learning Data Mining

The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques,