Item #:
107702167
Mathematics for Machine Learning
89% of respondents would recommend this to a friend
Item #:
107702167
USD 147
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This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites
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What Stands Out
Comprehensive Coverage
This book provides an in-depth exploration of essential mathematical concepts for machine learning, ensuring readers gain a thorough understanding of underlying principles needed to excel.
Practical Applications
Each chapter connects theoretical mathematics to real-world machine learning applications, helping readers apply concepts effectively, bridging the gap between theory and practice.
Accessible Style
Written with clarity and precision, this hardcover edition caters to both beginners and experienced practitioners, making complex topics more approachable while maintaining rigorous academic standards.
Product Details
- Covers fundamental mathematical tools essential for understanding machine learning
- Integrates mathematical concepts with machine learning methods like linear regression, principal component analysis, etc.
- Helps bridge the gap between mathematical and machine learning texts
- Suitable for data science, computer science students, or professionals
- Includes worked examples, exercises, and programming tutorials for practical understanding
- Provides a self-contained approach with minimal prerequisites
| Publisher | Cambridge University Press |
| Publication date | 23 April 2020 |
| Language | English |
| Print length | 390 pages |
| ISBN-10 | 1108470041 |
| ISBN-13 | 978-1108470049 |
| Item weight | 980 g |
| Dimensions | 17.78 x 2.82 x 25.4 cm |
| Part of series | Studies in Natural Language Processing |
Who Should Buy?
-
Aspiring Data Scientists
Those looking to build a solid foundation in mathematics essential for data science and machine learning applications.
-
Graduate Students
Perfect for graduate studies where a rigorous understanding of mathematical concepts relevant to machine learning is required.
-
Industry Professionals
Professionals transitioning into machine learning roles who need to refresh or enhance their mathematical skills for practical application.
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Beginner Learners
Complete beginners in mathematics may struggle with the advanced concepts presented without prior foundational knowledge.
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Editorial Review
Mathematics For Machine Learning is an essential guide published by Cambridge University Press that elegantly bridges the gap between mathematical principles and their application in machine learning. The book, consisting of 390 pages, is crafted for those with a basic understanding of algebra, statistics, and calculus, making it ideal for high school graduates eager to delve into the intricacies of machine learning. Readers appreciate the clear structure and helpful illustrations which enhance comprehension of complex concepts like PCA and L2 norm. The margins are designed for note-taking, adding an interactive element to the learning experience. Users have highlighted the quality of the content, praising its ability to simplify mathematical learning for aspiring machine learning enthusiasts.
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Pros
- Excellent resource for beginners in machine learning
- Clear explanations of complex mathematical concepts
- Well-structured with helpful illustrations
- Interactive margins for personal notes
- Covers all important math topics effectively
Cons
- Minor issue with glue on the first pages
Product Price History
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USD 147
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Features & Benefits
- Textbook covers the fundamental mathematical tools required for understanding machine learning
- Introduces mathematical concepts with minimum prerequisites
- Uses concepts to derive four central machine learning methods
- Includes worked examples and exercises in every chapter
- Programming tutorials offered on book's website
- Suitable for students with mathematical background or those learning the mathematics for the first time
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