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42141106
Mathematics for Machine Learning, (Paperback)
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Item #:
42141106
CHF 66
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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 offers an in-depth exploration of essential mathematical concepts, ensuring learners grasp necessary skills for machine learning applications.
Practical Applications
Real-world examples illuminate complex theories, making abstract math accessible and relevant for aspiring data scientists and ML practitioners.
User-friendly Format
The paperback format allows for easy handling and note-taking, supporting readers in their journey through intricate mathematical frameworks consistently.
Product Details
- Covers fundamental mathematical tools for understanding machine learning
- Integrates mathematical concepts and machine learning methods for easy comprehension
- Suitable for data science, computer science students, and professionals
- Provides derivations for central machine learning methods like linear regression and support vector machines
- Includes worked examples, exercises, and programming tutorials on the book's website
- Serves as a self-contained textbook requiring minimal prerequisites
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | April, 2020 |
| Pages | 398 |
| Reading level | General |
| Subgenre | Artificial Intelligence |
| Series title | No Series |
| Edition | 1 |
| Publisher | Cambridge University Press |
| Original languages | English |
| Language | English |
| Edu focus | Mathematics |
| Educational level | General |
| Is collectible | N |
| Binding type | Perfect Binding |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 6.93 x 0.87 x 9.84 in (17.6 x 2.2 x 25 cm) |
| Assembled product weight | 1.85 lb (840 grams) |
| Bisac subject heading | Computers |
Who Should Buy?
-
Aspiring Data Scientists
Ideal for those starting a career in data science seeking foundational mathematical knowledge for machine learning.
-
Computer Science Students
Perfect for students studying computer science who need to understand mathematical concepts applied in machine learning.
-
Researchers in AI
Beneficial for researchers aiming to deepen their understanding of the mathematical underpinnings of machine learning algorithms.
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Beginner Math Learners
Not suitable for individuals without prior mathematical knowledge as it requires some familiarity with advanced concepts.
Product Description
About This Item
Are you looking for a comprehensive guide to understanding the mathematical concepts behind machine learning? Look no further than "Mathematics for Machine Learning Paperback." Written by renowned authors Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong, this book is a must-have for anyone interested in the field of machine learning. ISBN: 9781108455145 In today's digital age, machine learning has become an integral part of various industries, including finance, healthcare, and technology. However, to truly grasp the concepts and algorithms used in machine learning, a solid foundation in mathematics is essential. This paperback edition of "Mathematics for Machine Learning" offers a comprehensive exploration of the mathematical principles that underpin the field. From linear algebra and calculus to probability and statistics, this book covers all the essential topics necessary for understanding and implementing machine learning algorithms. Whether you're a beginner or have some background knowledge in mathematics, this book has something to offer for everyone.
The authors have taken great care to explain complex mathematical concepts in a clear and concise manner, making it accessible to readers of all levels. By delving into the core principles of machine learning, this book provides a solid framework for understanding the algorithms and techniques used in this rapidly evolving field. With practical examples and exercises, readers will also gain hands-on experience in applying mathematical concepts to real-world machine learning problems. Stay up-to-date with the latest advancements in machine learning with this publication, as it was last revised and published on April 23, 2020. This ensures that you're getting the most relevant and up-to-date information in the field. Whether you're a student, a professional, or simply curious about the world of machine learning, "Mathematics for Machine Learning Paperback" is an indispensable resource.
Expand your knowledge and master the mathematical foundations of this exciting field today. Keywords: Mathematics for Machine Learning, Machine Learning Paperback, Mathematics, Machine Learning, Paperback.
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Marc Peter Deisenroth All Books Editorial Review
Mathematics for Machine Learning (Paperback) is a comprehensive non-fiction book published by Cambridge University Press in April 2020. Spanning 398 pages, this text is aimed at a general audience interested in the intersection of mathematics and artificial intelligence. The book employs a well-structured approach to illuminate core mathematical concepts essential for machine learning, making it an ideal resource for those looking to deepen their understanding of this pivotal subject. Its perfect binding ensures durability, while the dimensions make it convenient for both reading and reference. Readers appreciate its clarity and practical insights into mathematical applications for computing.
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Pros
- Comprehensive coverage of essential mathematical concepts
- Well-structured for general understanding
- Ideal for those interested in artificial intelligence
- Durable perfect binding for longevity
- Includes practical insights for real-world applications
Cons
- Not a collectible edition
Product Price History
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CHF 66
Order now and get it around Friday, October 16
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Only 12 items left in stock.
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Features & Benefits
- Bridges the gap between mathematical and machine learning texts
- Introduces mathematical concepts with minimum prerequisites
- Derives four central machine learning methods
- Helps build intuition and practical experience
- Includes worked examples and exercises
- Programming tutorials available on the website
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