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Item #:
16258219
Understanding Machine Learning
87% of respondents would recommend this to a friend
Item #:
16258219
€ 62
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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
| 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?
-
Beginner Students
Ideal for students starting their journey in machine learning, offering fundamental concepts and clear explanations.
-
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.
-
Advanced Researchers
Not suitable for experts seeking cutting-edge research or advanced methodologies beyond introductory frameworks and concepts.
Product Description
Understanding Machine Learning
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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
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