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Item #:
36959988
Machine Learning: An Algorithmic Perspective, Second Edition Chapman & HallCRC Machine Learning & Pattern Recognition 2nd Edition
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36959988
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What Stands Out
Comprehensive Coverage
This edition offers an extensive overview of machine learning algorithms, providing readers with deep insights and a clear understanding, essential for both beginners and experienced practitioners in the field.
Real-World Applications
Includes practical examples and case studies that demonstrate algorithm implementation, helping readers to bridge the gap between theory and real-world application, enhancing their problem-solving skills.
Updated Content
Features the latest advancements in machine learning technology and methodologies, ensuring that readers have access to current knowledge, which is crucial for staying relevant in a rapidly evolving field.
Product Details
| Publisher | Chapman and Hall/CRC |
| Publication date | October 8, 2014 |
| Edition | 2nd |
| Language | English |
| Print length | 458 pages |
| ISBN-10 | 1466583282 |
| ISBN-13 | 978-1466583283 |
| Item Weight | 2 pounds (910 grams) |
| Dimensions | 7 x 1.25 x 10 inches (17.8 x 3.2 x 25.4 cm) |
| Part of series | Chapman & Hall/Crc Machine Learning & Pattern Recognition |
Who Should Buy?
-
Students
Ideal for university students studying machine learning, providing a comprehensive foundation in algorithms and theoretical concepts.
-
Practitioners
Beneficial for data scientists and machine learning practitioners seeking to deepen their algorithmic knowledge and application.
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Researchers
Useful for researchers and academics looking for detailed algorithmic insights and extensive references in the field.
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Beginners
Not suitable for complete beginners, as it assumes prior knowledge of programming and mathematics in machine learning.
Product Description
Machine Learning: An Algorithmic Perspective, Second Edition Chapman & HallCRC Machine Learning & Pattern Recognition 2nd Edition
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Machine Theory Editorial Review
This book provides an in-depth explanation of important algorithms in the field of machine learning. The author has a great implementation-focused view of the innards of machine learning algorithms. Although some of the more challenging concepts are simplified, the book has a way of making easy concepts difficult. The book covers all the fundamentals and is a great choice for a primary text in machine learning. The author's use of numpy sometimes leads to confusion, but the second edition is an improvement over the first. It would be a great asset for anyone wanting to get into advanced Python addressing.
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Pros
- Intuitive explanations of important algorithms.
- Provides access to great online resources.
- An improvement over the first edition.
- Great choice for a primary text in machine learning.
- Covers all the fundamentals.
Cons
- Some errors and confusing math notations.
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