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- Mathematics for Machine Learning
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منتج #:
88199085
Mathematics for Machine Learning
85% من المشترين سيوصون بهذا المنتج لصديق
منتج #:
88199085
BHD 33
تفاصيل السعر
باستثناء رسوم الشحن والجمارك ( سيتم احتساب رسوم الشحن والجمارك عند إتمام الشراء )
*سيتم استيراد جميع العناصر من المملكة المتحدة
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كمية:
اطلب الآن واحصل عليه حول الجمعة, أكتوبر 23
معاملة آمنة
تعمل يوباي جاهدة لحماية أمنك وخصوصيتك. يضمن نظام أمان الدفع المتقدم لدينا السرية من خلال تشفير معلوماتك أثناء النقل باستخدام بروتوكولات AES (معايير التشفير المتقدمة) وSSL (طبقة المنافذ الآمنة). تفاصيل الدفع الخاصة بك آمنة بنسبة %100 لأننا لا نشارك تفاصيل الدفع الخاصة بك مع بائعين تابعين لجهات خارجية
أفضل شركائنا اللوجستيين
This self-contained textbook bridges the gap between mathematical and machine learning texts by introducing mathematical concepts with a minimum of prerequisites.
شحن
سريع
استرجاع
مجاني*
تغليف آمن
منتجات أصلية %100
الامتثال لمعيار PCI DSS
حاصل على شهادة ISO 27001
أبرز ما يلفت الانتباه
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.
تفاصيل المنتج
- 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 |
من يجب أن يشتري؟
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Aspiring Data Scientists
Provides foundational knowledge in mathematics necessary for understanding machine learning algorithms and data analysis.
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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.
وصف المنتج
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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Product Buying Guide
Are you a data science or computer science student or professional looking to understand the core mathematical concepts essential for machine learning? The Mathematics for Machine Learning 1st Edition is a self-contained textbook that bridges the gap between mathematical and machine learning texts, making it easier to grasp the fundamental tools needed for machine learning. Whether you have a mathematical background or are learning these concepts for the first time, this book offers a practical and intuitive approach to understanding and applying mathematical concepts in the context of machine learning.
Product Specifications
- Author: Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong
- Publication Date: May 8, 2020
- Publisher: Cambridge University Press
- Paperback: 402 pages
- Language: English
- ISBN-10: 110845514X
- ISBN-13: 978-1108455145
Key Features
- Bridges the gap between mathematical and machine learning texts
- Introduces mathematical concepts with minimal prerequisites
- Derives central machine learning methods including linear regression, principal component analysis, Gaussian mixture models, and support vector machines
- Includes worked examples and exercises for practical understanding
- Offers programming tutorials on the book's website
Usage Scenarios
- Ideal for data science or computer science students seeking to understand mathematical concepts essential for machine learning
- Suitable for professionals looking to enhance their understanding and practical experience with applying mathematical concepts in the context of machine learning
- Useful for individuals with a mathematical background as well as for those learning these concepts for the first time
Usage Scenarios
- Competitor 1
- Competitor 2
- Competitor 3
Some User Review
- The book effectively bridges the gap between mathematical concepts and machine learning, providing a straightforward approach to understanding and applying these concepts.
- The inclusion of programming tutorials on the book's website is exceptionally helpful for practical implementation and reinforcement of the learned concepts.
- The worked examples and exercises contribute significantly to building a strong understanding of the mathematical tools in the context of machine learning.
Competitors
- The price of the Mathematics for Machine Learning 1st Edition is competitive compared to similar textbooks in the market, offering a cost-effective choice for learning essential mathematical concepts for machine learning.
Buying Considerations
- Consider your level of mathematical background or familiarity with the concepts when purchasing this book to ensure that it meets your learning needs and objectives.
- Take advantage of the programming tutorials offered on the book's website to enhance practical experience and implementation of the mathematical concepts in machine learning.
Conclusion
The Mathematics for Machine Learning 1st Edition is a valuable resource for students and professionals seeking a comprehensive understanding of the fundamental mathematical tools required for machine learning. With its practical approach, intuitive explanations, and inclusion of programming tutorials, this self-contained textbook is an excellent choice for anyone looking to bridge the gap between mathematical and machine learning texts.
عرض أقلAre you a data science or computer science student or professional looking to understand the core mathematical concepts essential for machine learning? The Mathematics for Machine Learning 1st Edition is a self-contained textbook that bridges the gap between mathematical and machine learning texts, making it easier to grasp the fundamental tools needed for machine learning. Whether you have a mathematical background or are learning these concepts for the first time, this book offers a practical and intuitive approach to understanding and applying mathematical concepts in the context of machine learning. Continue Reading
أسئلة العملاء & الإجابات
-
سؤال:
كيف تتسوق Mathematics for Machine Learning عبر الانترنت من يوباى?
إجابه: من السهل التسوق في Mathematics for Machine Learning عبر الإنترنت من يوباي. كل ما عليك فعله هو البحث عن المنتج واختيار طريقة الشحن الخاصة بك أثناء الدفع وسيتم توصيله الى عنوانك -
سؤال:
هل Mathematics for Machine Learning متوفر للتسوق عبر الإنترنت في Bahrain؟
إجابه: نعم ، في يوباي Bahrain هذا المنتج متاح لك للتسوق بسعر مناسب. Mathematics for Machine Learning غير متوفر محليًا ولكن يمكنك الوثوق بنا بخدماتنا للشحن السريع. -
سؤال:
كم من الوقت يستغرق الحصول على المنتج بعد تقديم الطلب؟
إجابه: يختلف وقت تسليم المنتج الذي طلبته حسب ما طلبته وطريقة الشحن التي اخترتها. يتم ذكر وقت التسليم المقدر أثناء عملية الدفع ، لذا كن مرتاحًا أثناء التسوق.
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.
مراجعات العملاء وتقييماتهم
1032 تقييمات العملاء
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5 نجمة
75%
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4 نجمة
15%
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3 نجمة
5%
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2 نجمة
2%
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1 نجمة
3%
أضف تقييم لهذا المنتج
شارك أفكارك مع عملاء آخرين
إيجابيات
- 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
سلبيات
- Minor glue issue on first pages doesn't affect usability
تاريخ سعر المنتج
معلومات مهمة
- القيود: بالنسبة للمنتجات التي يتم شحنها دولياً، يُرجى ملاحظة أن أي ضمان من الشركة المصنعة قد لا يكون صالحاً؛ قد لا تتوفر خيارات خدمة الشركة المصنعة؛ قد لا تكون أدلة المنتج والتعليمات وتحذيرات السلامة مكتوبة بلغة بلد المقصد؛ قد لا يتم تصميم المنتجات (والمواد المصاحبة لها) وفقاً لمعايير بلد الوجهة والمواصفات ومتطلبات الملصقات؛ وقد لا تتوافق المنتجات مع الجهد الكهربي المستخدم في بلد الوجهة والمعايير الكهربائية الأخرى (تتطلب استخدام محوّل كهربي أو جهاز تحويل إذا كان ذلك مناسباً). المستلم مسؤول عن ضمان إمكانية استيراد المنتج بشكل قانوني إلى بلد الوجهة. عند الطلب من يوباي أو الشركات التابعة لها، يكون المستلم هو المستورد المسجل ويجب أن يلتزم بجميع القوانين واللوائح الخاصة ببلد الوجهة.
- ليست كل المنتجات المدرجة على يوباي معروضة للبيع، لأن يوباي هو محرك بحث عالمي. المنتجات تخضع للوائح التصدير / التجارة.
BHD 33
اطلب الآن واحصل عليه حول الجمعة, أكتوبر 23
هذا المنتج غير ممنوع في بلدي. (الرجاء الضغط على الرابط أعلاه إذا لم يكن هذا المنتج ممنوعاً في بلدك ، لذلك سيقوم فريقنا بمراجعته والسماح به.)
كمية:
معاملة آمنة
نوفر لك مدفوعات مشفّرة، وحماية متكاملة للمشتري، مع الالتزام بمعايير PCI DSS وشهادة ISO 27001:2022 لضمان أعلى مستويات الأمان في كل عملية شراء.
أفضل شركائنا اللوجستيين
أسرع توصيل عبر الحدود
المميزات والفوائد
- 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
ضمان Ubuy
تسوّق بثقة مع منتجات أصلية %100، ومدفوعات آمنة متوافقة مع معيار PCI DSS، وحماية بيانات معتمدة وفق ISO 27001، وشحن دولي سريع، وإرجاع مجاني*، وتغليف آمن لكل طلب.