Item #:
107702167
Mathematics for Machine Learning
Item #:
107702167
BTN 12191
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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
| 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.
Product Description
Mathematics for Machine Learning
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Customer Questions & Answers
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Question:
What topics are covered in the textbook?
Answer: The textbook covers linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics. -
Question:
Who is the textbook suitable for?
Answer: The textbook is suitable for students and professionals with a mathematical background or those learning the mathematics for the first time. -
Question:
Are there any additional resources offered?
Answer: Yes, programming tutorials are offered on the book's website.
Editorial Review
**** The "Mathematics for Machine Learning" hardcover presents an insightful journey for individuals with a high school math background keen to delve into the complexities of machine learning. Reviewers resonate with the book's capacity to clarify key mathematical concepts, such as PCA, L2 norms, and rank, which often serve as jargons among machine learning engineers. The text is praised for its approachable style, effectively bridging the gap between foundational mathematics and its practical applications in the machine learning realm. Readers appreciate the clear structuring and visual layout of the content, which enhances readability and facilitates quicker absorption of the material. The use of margin space for annotations and footnotes is particularly highlighted as a feature that enriches the learning experience. Although the book is not intended for complete beginners—suggesting readers have some familiarity with algebra, statistics, and calculus—it serves as an essential resource for those who wish to strengthen their understanding of the mathematics that underpins machine learning. While some users express a desire for an updated edition that steps beyond conventional methods, the Consensus remains that this book is an invaluable asset for machine learning enthusiasts eager to grasp the math that shapes the technology. **
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Pros
- Great for individuals with a high school math background.
- Clear explanations of complex mathematical jargon.
- Well-structured and easy to read with a visually appealing layout.
- Adequate margin space for notes and sticky notes.
- Comprehensive coverage of essential mathematical concepts in machine learning.
Cons
- Assumes foundational knowledge in algebra, statistics, and calculus.
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BTN 12191
Order now and get it around Sunday, August 16
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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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