Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) 4th Edition
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition... teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) 4th Edition
Nº de artículo: 4574364

Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) 4th Edition

Nº de artículo: 4574364

GTQ 406

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Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition... teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
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What Stands Out

Comprehensive Coverage
This edition offers extensive insights into data mining techniques, making it perfect for both beginners and advanced practitioners looking to harness machine learning tools effectively.
Real-World Applications
The book provides practical examples and case studies, addressing real-world problems and demonstrating how data mining can be applied to various industries.
Expert Contributions
Authored by leading experts, it combines theoretical knowledge with practical savvy, ensuring readers gain a deep understanding of machine learning's best practices.

Detalles de producto

Get the latest edition of Data Mining: Practical Machine Learning Tools and Techniques at Ubuy, your Guatemala for all your book needs. Shop now!
  • Thorough grounding in machine learning concepts and practical advice for real world data mining
  • Extensive updates reflecting modernizations and new chapters on probabilistic methods and deep learning
  • Accompanied by a new version of the popular WEKA machine learning software from the University of Waikato
  • Comprehensive teaching resource with Powerpoint slides, online appendix, and table of contents on book companion website
  • Concrete tips and techniques for performance improvement in machine learning methods
  • Includes downloadable WEKA software toolkit, open access online courses, and reviews of the 1st edition
Publisher Morgan Kaufmann
Publication date December 1, 2016
Edition 4th
Language English
Print length 654 pages
ISBN-10 0128042915
ISBN-13 978-0128042915
Item Weight 2.31 pounds (1.05 kg)
Dimensions 7.5 x 1.48 x 9.25 inches (19.1 x 3.8 x 23.5 cm)
Part of series The Morgan Kaufmann Series in Data Management Systems

Who Should Buy?

Suitable For
  • Data Scientists

    Ideal for data scientists looking for comprehensive insights and practical tools to implement machine learning algorithms.

  • Academic Researchers

    Suitable for researchers needing a solid resource for understanding data mining methods and practical applications in studies.

  • Students

    Beneficial for university students in data science or related fields, providing clear explanations and relevant machine learning techniques.

Not Suitable For
  • Complete Beginners

    Not suitable for those with no prior knowledge of data mining or machine learning concepts and terminologies.

DESCRIPCIÓN DEL PRODUCTO

Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) 4th Edition

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Intelligence & Semantics Editorial Review

The book "Data Mining: Practical Machine Learning Tools and Techniques" by Ian H. Witten, Eibe Frank, and Mark A. Hall is a comprehensive guide that includes practical descriptions and examples for most machine learning methods and algorithms. It is an easy-to-read book that provides a good introduction to machine learning for beginners, although it has some issues that may limit its usefulness. One of the main problems is that the language used in the book is very esoteric, making it difficult to follow for those who are not familiar with the terminology. Additionally, the book's structure is somewhat confusing and disorganized, making it hard to gain in-depth knowledge of any particular method. Customers have reported dissatisfaction with the software Weka, which is used in the book, stating that it is not ideal for big data and has a poorly designed user interface that accepts minimal hyperparameters. The GUI interface may start to glitch, rendering the screen choppy. The book's author, Ian Witten, also received criticism from some customers for being cryptic and unhelpful in his Youtube videos.

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ventajas

  • Comprehensive guide that includes practical descriptions and examples for most machine learning methods and algorithms
  • Easy to understand and read

Contras

  • Language used in the book is very esoteric, making it difficult to follow for those who are not familiar with the terminology

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