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54279678
Machine Learning: An Algorithmic Perspective (Chapman & Hall/Crc Machine Learning & Pattern Recognition)
79% of respondents would recommend this to a friend
Item #:
54279678
€ 90
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Machine Learning: An Algorithmic Perspective is the text that demonstrates how to use algorithms while providing the background needed to understand how and why they work.
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What Stands Out
Comprehensive Coverage
This book offers a detailed exploration of machine learning algorithms, catering to both beginners and experienced practitioners, making it a go-to resource for academics and professionals alike.
Algorithm Focus
Focusing on algorithmic perspectives, it equips readers with a solid understanding of how algorithms function, empowering them to solve complex problems efficiently and innovatively.
Practical Applications
Real-world case studies and practical examples bridge theory and practice, helping readers apply concepts effectively while enhancing problem-solving skills in machine learning.
Product Details
| Publisher | Chapman and Hall/CRC |
| Publication date | April 1, 2009 |
| Edition | 1st |
| Language | English |
| Print length | 406 pages |
| ISBN-10 | 1420067184 |
| ISBN-13 | 978-1420067187 |
| Item Weight | 1.5 pounds (680 grams) |
| Dimensions | 6.75 x 1 x 9.75 inches (17.1 x 2.5 x 24.8 cm) |
Who Should Buy?
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Graduate Students
Ideal for graduate students pursuing advanced studies in machine learning and requiring a comprehensive understanding of algorithms.
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Data Scientists
Data scientists looking to enhance their algorithmic knowledge and practical implementation of machine learning techniques.
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Research Professionals
Research professionals focused on developing new machine learning techniques and looking for theoretical and practical insights.
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Beginner Users
May overwhelm beginners with its technical depth; not suitable for those with little background in machine learning.
Product Description
Machine Learning: An Algorithmic Perspective (Chapman & Hall/Crc Machine Learning & Pattern Recognition)
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Data Mining Editorial Review
If you're looking for an introductory machine learning book with a balance of theory and algorithmic descriptions, then Stephen Marsland's Machine Learning: An Algorithmic Perspective is for you. It presents a wide range of ML techniques, including artificial neural networks (ANNs), support vector machines (SVMs), and Bayesian networks, and covers topics not found in other introductory ML books. The author's writing is clear and not condescending, with an emphasis on building understanding gradually. It's well-suited for Junior/Senior level college students who have a working knowledge of Python programming and solid foundations in mathematics, including statistics. However, the book is not without its flaws. Readers should be warned that some of the codes in the book have bugs, and not all of the mathematical concepts are correctly presented, such as the universal approximation theorem for ANNs.
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Pros
- Presents a wide range of ML techniques and covers topics not found in other introductory ML books
- Clear and not condescending writing style
- Builds understanding gradually
- Well-suited for Junior/Senior level college students who have a working knowledge of Python programming and solid foundations in mathematics, including statistics
Cons
- Some of the codes in the book have bugs
Product Price History
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€ 90
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Features & Benefits
- Combines theory with practical examples for a deeper understanding of machine learning.
- Covers a wide range of topics including neural networks, optimization, and reinforcement learning.
- Strikes a balance between academic rigor and accessible information.
- Includes code examples and a companion website with Python implementations.
- Demonstrates applicability across multiple fields including finance, biology, and engineering.
- Ideal for students with basic mathematical knowledge seeking to apply machine learning algorithms.
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