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36960053
Chapman & Hall/CRC Machine Learning & Pattern Recognition: Machine Learning: An Algorithmic Perspective, Second Edition (Hardcover)
90% of respondents would recommend this to a friend
Nº de artículo:
36960053
PYG 1132146
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Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning.
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What Stands Out
Comprehensive Coverage
Offers a thorough exploration of machine learning algorithms, ensuring readers gain a solid understanding of concepts and applications across various domains.
Practical Examples
Incorporates real-world case studies and practical examples, enabling users to apply theoretical knowledge to solve actual problems in machine learning.
Expert Authors
Written by recognized experts in the field, providing authoritative insights and advanced techniques that enhance the learning experience for both beginners and seasoned practitioners.
Detalles de producto
- Proven, hands-on approach for computer science students without a strong statistical foundation
- New chapters on deep belief networks and Gaussian processes in the second edition
- Reorganized chapters for a more natural flow of content
- Improved material on support vector machine, random forests, accuracy methods, and more
- Encourages students to practice with the provided code and includes detailed examples, further reading, and problems for each chapter
- All code used in examples available on the author's website
| Book format | Hardcover |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | October, 2014 |
| Pages | 458 |
| Reading level | Undergraduate |
| Series title | Chapman & Hall/CRC Machine Learning & Pattern Recognition |
| Number in series | 2 |
| Edition | 2nd Edition |
| Publisher | CRC Press |
| Original languages | English |
| Language | english |
| Edu focus | Mathematics |
| Educational level | College |
| Is collectible | N |
| Assembled product height | 10 in |
| Assembled product weight | 2.24 lb (1.02 kg) |
| Bisac subject heading | Business & Economics, Computers |
Who Should Buy?
-
Students
Ideal for undergraduate and graduate students studying machine learning concepts in computer science or data science fields.
-
Professionals
Designed for data scientists and machine learning engineers seeking to deepen their algorithmic knowledge and practical application.
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Researchers
Helpful for researchers exploring advanced machine learning techniques and wanting thorough algorithm explanations and examples.
-
Beginners
Not suitable for those with no prior knowledge of programming or machine learning concepts, as it is quite technical.
DESCRIPCIÓN DEL PRODUCTO
About This Item
Introducing the Chapman & HallCRC Machine Learning & Pattern Recognition: Machine Learning: An Algorithmic Perspective, Second Edition. This comprehensive book is a must-have for anyone interested in the fascinating field of machine learning and pattern recognition. Authored by Stephen Marsland, a renowned expert in the field, this book provides a thorough understanding of the fundamentals of machine learning. With its clear and concise writing style, it caters to beginners as well as advanced users. This second edition features updated content and algorithms, making it a valuable resource for both students and practitioners.
Whether you're a computer science student, a researcher, or a professional in the industry, this book will guide you through the world of machine learning with ease. Learn about various machine learning algorithms, such as supervised and unsupervised learning techniques, and gain insights into pattern recognition techniques. Discover how machine learning is applied in different domains, like healthcare and finance, and explore topics like natural language processing and big data analytics. With its focus on practical application, this book offers real-world examples and case studies to help you understand how machine learning algorithms are implemented. Additionally, you'll find tutorials and courses that accompany the book, providing a comprehensive learning experience. The Chapman & HallCRC Machine Learning & Pattern Recognition: Machine Learning: An Algorithmic Perspective, Second Edition is available in a durable hardcover format, ensuring that it will withstand constant use and remain a valuable reference in your library. Don't miss out on this opportunity to master machine learning and pattern recognition.
Order your copy of the Chapman & HallCRC Machine Learning & Pattern Recognition: Machine Learning: An Algorithmic Perspective, Second Edition today and unlock the power of machine learning in your projects and research.
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Stephen Marsland All Books Editorial Review
Chapman & Hall/CRC Machine Learning & Pattern Recognition: Machine Learning: An Algorithmic Perspective, Second Edition (Hardcover) is an essential non-fiction title published by CRC Press in October 2014, providing a comprehensive overview of machine learning concepts suitable for undergraduate readers. This book spans 458 pages and is part of the esteemed series focusing on computing and internet applications. It effectively intertwines mathematics with real-world computing scenarios, making it particularly valuable for college students seeking to deepen their understanding of algorithmic approaches in machine learning. The hardcover format ensures durability, making it a reliable resource for academic use.
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ventajas
- Broad coverage of machine learning concepts
- Ideal for undergraduate students
- Durable hardcover format
- Part of a reputable series
- Integrates math with practical applications
Contras
- Some readers may find it dense to comprehend
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PYG 1132146
Haz tu pedido ahora y recíbelo por ahí Viernes, Octubre 30
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características y beneficios
- Proven, hands-on approach for students without a strong statistical foundation
- Helps students understand the algorithms of machine learning
- Includes new chapters on deep belief networks and Gaussian processes
- Reorganized chapters for a more natural flow of content
- Improved code, including better use of naming conventions in Python
- Suitable for both introductory and advanced courses
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