Item #:
107702167
Mathematics for Machine Learning
89% of respondents would recommend this to a friend
Item #:
107702167
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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.
-
Beginner Learners
Complete beginners in mathematics may struggle with the advanced concepts presented without prior foundational knowledge.
Product Description
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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.
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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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