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Nº de artículo:
88199085
Mathematics for Machine Learning
87% of respondents would recommend this to a friend
Nº de artículo:
88199085
USD 70
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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 Content
Covers essential mathematical concepts crucial for understanding machine learning, making it suitable for beginners and experienced professionals alike.
Practical Applications
Includes real-world examples and applications, helping readers bridge the gap between theory and practice in machine learning.
Accessible Learning
Written in a clear and engaging style, enhancing learning for readers from varied mathematical backgrounds, ensuring broad accessibility.
Detalles de producto
- The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
| Publisher | Cambridge University Press |
| Publication date | 23 April 2020 |
| Edition | 1st |
| Language | English |
| Print length | 390 pages |
| ISBN-10 | 110845514X |
| ISBN-13 | 978-1108455145 |
| Item weight | 800 g |
| Dimensions | 17.78 x 2.24 x 25.4 cm |
| Part of series | Studies in Natural Language Processing |
Who Should Buy?
-
Aspiring Data Scientists
Provides foundational knowledge in mathematics necessary for understanding machine learning algorithms and data analysis.
-
Engineering Students
Ideal for students studying engineering who need to apply mathematical concepts in real-world machine learning applications.
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Self-Learners
Perfect for individuals pursuing self-education in machine learning and requiring a strong mathematical background.
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Beginner Programmers
Not suited for those with no programming experience, as the focus is on mathematics rather than coding skills.
DESCRIPCIÓN DEL PRODUCTO
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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Higher Education Editorial Review
Mathematics For Machine Learning is an essential resource published by Cambridge University Press that caters to those with a high school-level math background eager to delve into machine learning. This book, structured over 390 pages, focuses on essential mathematical concepts such as PCA, L2 norm, and the underlying principles guiding machine learning technologies. Users appreciate its clarity and simple illustrations, making it a fantastic choice for quickly grasping complex topics. The well-designed layout and generous margins allow for easy note-taking, which enhances the learning experience. It provides a solid foundation for connecting algebra, statistics, and basic calculus with machine learning, making it a valuable tool for beginners as well as enthusiasts seeking to understand the subject deeper.
Customer Reviews & Ratings
1030 valoraciones de los clientes
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5 estrella
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4 estrella
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3 estrella
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ventajas
- Well-structured content enhances understanding of concepts
- Clear illustrations aid in quick comprehension
- Generous margins for note-taking and annotations
- Perfect for beginners with foundational math knowledge
- Covers a wide range of essential topics in machine learning
Contras
- Minor glue issue on first pages doesn't affect usability
Product Price History
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características y beneficios
- 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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