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Item #:
18149781
Mathematics for Machine Learning
85% of respondents would recommend this to a friend
Item #:
18149781
PKR 21683
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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
This book covers mathematical concepts essential for machine learning thoroughly, making it suitable for beginners to experts seeking a solid foundation in statistics, linear algebra, and calculus.
Practical Applications
Each chapter includes real-world examples and exercises, allowing readers to see how mathematical theories apply directly to modern machine learning problems, enhancing practical understanding.
Accessible Learning
Written in an engaging style, this textbook simplifies complex topics, ensuring that readers from diverse educational backgrounds can grasp fundamental concepts critical for advancing in the field of machine learning.
Product Details
- 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 | April 23, 2020 |
| Edition | 1st |
| Language | English |
| Print length | 398 pages |
| ISBN-10 | 110845514X |
| ISBN-13 | 978-1108455145 |
| Item Weight | 1.76 pounds (800 grams) |
| Dimensions | 7 x 0.88 x 10 inches (17.8 x 2.2 x 25.4 cm) |
| Part of series | Studies in Natural Language Processing |
Who Should Buy?
-
Aspiring Data Scientists
This product offers essential mathematical foundations needed to understand and implement machine learning algorithms effectively.
-
University Students
Ideal for college students pursuing degrees in computer science or related fields, enhancing their understanding of machine learning principles.
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Professionals Upskilling
Suitable for industry professionals looking to improve their mathematical skills related to machine learning applications and frameworks.
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Beginner Mathematicians
Not ideal for those with little to no mathematical knowledge, as it assumes a foundational understanding of advanced concepts.
Product Description
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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Computer Vision & Pattern Recognition Editorial Review
Mathematics for Machine Learning offers a structured approach for understanding the mathematical concepts crucial to machine learning. It caters to readers with varying backgrounds, emphasizing essential topics in linear algebra, calculus, and statistics. The book is well-organized and visually engaging, making complex ideas more accessible. The inclusion of supplementary material, such as freely available PDFs and Jupyter notebooks, significantly enhances the learning experience, allowing for practical exploration of topics covered. Readers appreciate that it doesn't assume advanced prior knowledge, though having some foundational math skills is beneficial. This book serves as a bridge between theoretical concepts and practical implementation in machine learning, guiding both beginners and experienced practitioners alike.
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Pros
- Well-structured content for varying backgrounds
- Free access to PDFs and Jupyter notebooks
- Visually engaging illustrations enhance understanding
- Clear explanations simplify complex topics
- Ideal for bridging theory and practice in ML
Cons
- Not comprehensive for advanced mathematical topics
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PKR 21683
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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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