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Item #:
192089962
Mathematics for Machine Learning
90% of respondents would recommend this to a friend
Item #:
192089962
SEK 860
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This self-contained textbook bridges the gap between mathematical and machine learning texts by introducing mathematical concepts with a minimum of prerequisites.
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What Stands Out
Comprehensive Coverage
Covers essential mathematical concepts like linear algebra, calculus, and statistics, tailored for machine learning applications, ensuring learners grasp foundational knowledge needed for algorithm implementation.
Practical Applications
Integrates real-world examples and practical exercises, bridging the gap between theory and practice, helping learners apply mathematical concepts directly to machine learning problems.
Accessible Learning
Designed for a broad audience, from beginners to advanced practitioners, featuring clear explanations and step-by-step guidance, making complex topics accessible without a heavy mathematical background.
Product Details
| Publisher | Cambridge University Press |
| Publication date | April 23, 2020 |
| Language | English |
| Print length | 390 pages |
| ISBN-10 | 110845514X |
| ISBN-13 | 978-1108455145 |
| Item Weight | 800 g |
| Dimensions | 7 x 0.88 x 10 inches (17.8 x 2.2 x 25.4 cm) |
Who Should Buy?
-
Aspiring Data Scientists
Ideal for individuals looking to understand the mathematical foundations crucial for data science and machine learning applications.
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Computer Science Students
Beneficial for students wanting to enhance their programming skills with essential mathematical concepts for advanced studies in AI.
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Industry Professionals
Useful for professionals seeking to upskill in machine learning, providing necessary mathematical knowledge for practical implementations.
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Complete Mathematics Novices
Not suitable for those without basic mathematical knowledge, as it presumes understanding of fundamental concepts.
Product Description
Mathematics for Machine Learning
About This Item
Introducing the Mathematics for Machine Learning 1st Edition As the field of machine learning continues to revolutionize various industries, it is essential to have a solid understanding of the mathematical concepts that underpin this powerful technology. The Mathematics for Machine Learning 1st Edition is a comprehensive textbook that covers all the key mathematical foundations needed for successful implementation and application of machine learning algorithms. With endorsements from esteemed experts in the field, such as Joelle Pineau from McGill University and Christopher Bishop from Microsoft Research Cambridge, this book comes highly recommended for both beginners and experienced machine learning researchers and engineers. This self-contained textbook is designed to be accessible to a wide range of readers, with a minimum of prerequisites. It starts with a thorough introduction to linear algebra, which serves as the basis for many machine learning techniques.
From there, it delves into topics such as analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics – all of which are crucial for developing a strong understanding of machine learning algorithms. Whether you are a student, a colleague, or simply someone interested in building a solid foundation in machine learning, this book will be an invaluable resource. It presents the necessary mathematical concepts in a clear and concise manner, making it easy to grasp complex ideas and apply them to real-world scenarios. The Mathematics for Machine Learning 1st Edition is not just a tutorial; it is a comprehensive reference text that you can turn to time and time again. It will help you gain a deeper understanding of the mathematical principles behind machine learning algorithms, enabling you to unlock the full potential of this transformative technology. Don't miss out on this essential resource for anyone interested in machine learning.
Order your copy of the Mathematics for Machine Learning 1st Edition today and take your understanding of this exciting field to new heights.
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English Edition Marc Peter Deisenroth Format: Paperback Applied Editorial Review
Mathematics for Machine Learning is a comprehensive resource published by Cambridge University Press on April 23, 2020. With a 390-page print length, this book provides an in-depth analysis of the mathematical principles important in machine learning. Written in English, it effectively bridges the gap between mathematics and practical machine learning applications. The dimensions of the book are 7 x 0.88 x 10 inches, making it a suitable reference for both students and professionals in the field. Although there are no reviews available, the content is structured to cater to a wide range of readers interested in enhancing their mathematical foundations for machine learning.
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Pros
- Comprehensive coverage of essential mathematical concepts
- Well-structured for machine learning applications
- Published by a reputable academic press
- Appropriate for both students and professionals
- Clear explanations of complex topics
Cons
- No available user reviews to gauge personal experiences
Product Price History
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SEK 860
Order now and get it around Thursday, October 15
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Features & Benefits
- Fundamental mathematical tools needed to understand machine learning
- Self-contained textbook for building intuition and practical experience
- Bridges gap between mathematical and machine learning texts
- Introduces mathematical concepts with a minimum of prerequisites
- Chapter exercises and programming tutorials available on the book's website
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