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Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) 4th Edition
88% of respondents would recommend this to a friend
XOF 43807
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What Stands Out
Product Details
| 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?
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Data Scientists
Ideal for data scientists looking for comprehensive insights and practical tools to implement machine learning algorithms.
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Academic Researchers
Suitable for researchers needing a solid resource for understanding data mining methods and practical applications in studies.
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Students
Beneficial for university students in data science or related fields, providing clear explanations and relevant machine learning techniques.
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Complete Beginners
Not suitable for those with no prior knowledge of data mining or machine learning concepts and terminologies.
Product Description
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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Pros
- Comprehensive guide that includes practical descriptions and examples for most machine learning methods and algorithms
- Easy to understand and read
Cons
- 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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