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Item #:
16258219
Understanding Machine Learning
87% of respondents would recommend this to a friend
Item #:
16258219
GBP 54
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What Stands Out
Clear Concepts
The book breaks down complex machine learning principles into understandable concepts, making it accessible for beginners while still valuable for more advanced readers.
Practical Examples
Includes numerous practical examples and case studies that illustrate real-world applications, helping readers grasp how to implement machine learning techniques effectively.
Comprehensive Coverage
Offers extensive coverage of various machine learning algorithms and techniques, providing readers with a well-rounded understanding of the field's challenges and innovations.
Product Details
- Introduces machine learning and its algorithmic paradigms in a principled way
- Provides extensive theoretical foundations and mathematical derivations of practical algorithms
- Covers topics like computational complexity, convexity, stability, stochastic gradient descent, neural networks, and more
- Explores emerging theoretical concepts like the PAC-Bayes approach and compression-based bounds
- Designed for advanced undergraduate or beginning graduate courses
- Accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering
| Publisher | Cambridge University Press |
| Publication date | July 17, 2014 |
| Edition | 1st |
| Language | English |
| Print length | 414 pages |
| ISBN-10 | 1107057132 |
| ISBN-13 | 978-1107057135 |
| Item Weight | 2 pounds (910 grams) |
| Dimensions | 7.2 x 1.1 x 10.24 inches (18.3 x 2.8 x 26 cm) |
Who Should Buy?
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Beginner Students
Ideal for students starting their journey in machine learning, offering fundamental concepts and clear explanations.
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Industry Professionals
Beneficial for professionals looking to enhance their understanding of machine learning algorithms and applications in practice.
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Self-Learners
Perfect for individuals learning independently who seek structured learning material with practical examples and exercises.
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Advanced Researchers
Not suitable for experts seeking cutting-edge research or advanced methodologies beyond introductory frameworks and concepts.
Product Description
Understanding Machine Learning
About This Item
Are you looking to understand the intricate world of machine learning? Look no further! The "Understanding Machine Learning: From Theory to Algorithms 1st Edition" is the perfect guide to help you unravel the complexities of this rapidly growing field. Whether you are a beginner or an experienced professional, this book provides a comprehensive introduction to machine learning concepts, theories, and algorithms. Dive into the fundamental principles and techniques that drive machine learning and equip yourself with the necessary knowledge to apply it in real-world scenarios. The book covers various topics, including supervised learning, unsupervised learning, reinforcement learning, and deep learning. Learn how to build models that can make predictions, classify data, and uncover patterns in vast amounts of information.
Discover how machine learning can be utilized in various industries, from finance to healthcare, to optimize processes and make data-driven decisions. What sets this book apart is its practical approach to machine learning. It not only explains the theories and algorithms but also provides examples that illustrate how they can be implemented. Follow step-by-step tutorials and gain hands-on experience to solidify your understanding of machine learning techniques. Aimed at both self-learners and students, this book is a valuable resource for anyone looking to delve into the world of machine learning.
Gain the skills you need to excel in this field and stay ahead in today's data-driven world. Make the "Understanding Machine Learning: From Theory to Algorithms 1st Edition" your go-to reference for all things machine learning. Start your journey towards becoming a machine learning expert today.
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Computer Vision & Pattern Recognition Editorial Review
This book is a solid resource for learning the theoretical aspects of machine learning. It explains basic concepts clearly, although there are some typos. If you're planning to do theoretical research in the field of machine learning, this book comes highly recommended. However, it may not be suitable for those looking for practical examples or a more intuitive understanding of the theory. Some readers found the use of mathematical notation and space-saving notation to be difficult to comprehend. Additionally, there were complaints about the quality of the paperback edition, with reports of torn covers and cheap paper. Overall, the book is well-written and informative, but it may not meet the needs of all readers.
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Pros
- Clear explanation of basic concepts
- Good resource for theoretical research in machine learning
- Informative small chapters that keep readers interested
Cons
- Lack of practical examples
Product Price History
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GBP 54
Order now and get it around Monday, October 26
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Features & Benefits
- Introduces machine learning in a principled way
- Covers a wide array of central topics not addressed by previous textbooks
- Discusses computational complexity, convexity, and stability
- Includes important algorithmic paradigms such as stochastic gradient descent
- Accessible to students and non-expert readers in various fields
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