Item #:
248368981
Mathematics for Machine Learning
Item #:
248368981
DZD 27275
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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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Product Details
- Covers fundamental mathematical tools essential for understanding machine learning
- Integrates mathematical concepts with machine learning methods like linear regression, principal component analysis, etc.
- Helps bridge the gap between mathematical and machine learning texts
- Suitable for data science, computer science students, or professionals
- Includes worked examples, exercises, and programming tutorials for practical understanding
- Provides a self-contained approach with minimal prerequisites
| Country of Origin | This item will be imported from JP |
| What is in the box | Mathematics for Machine Learning... For more details, please check description/product details |
Product Description
Mathematics for Machine Learning
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Customer Questions & Answers
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Question:
What topics are covered in the textbook?
Answer: The textbook covers linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics. -
Question:
Who is the textbook suitable for?
Answer: The textbook is suitable for students and professionals with a mathematical background or those learning the mathematics for the first time. -
Question:
Are there any additional resources offered?
Answer: Yes, programming tutorials are offered on the book's website.
English Edition Marc Peter Deisenroth , A. Aldo Faisal Editorial Review
Mathematics for Machine Learning, published by Cambridge University Press on April 23, 2020, is an essential read for anyone diving into the intersection of mathematics and machine learning. This well-structured book comprises 390 pages and is designed to tackle complex concepts in a digestible manner. Readers will appreciate the clarity of explanations that cater to individuals with various levels of mathematical backgrounds. The inclusion of helpful examples makes abstract ideas more concrete, thereby enhancing understanding amongst learners. This book's ISBN-13 is 978-1108470049, making it easy to find in various formats.
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Pros
- Well-structured content for diverse mathematical backgrounds
- Clear explanations enhance understanding of complex concepts
- Helpful examples make learning more engaging
- 390 pages of in-depth exploration
- Published by a reputable publisher, Cambridge University Press
Cons
- Some readers may find it dense in certain sections
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DZD 27275
Order now and get it around Wednesday, October 28
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Features & Benefits
- Textbook covers the fundamental mathematical tools required for understanding machine learning
- Introduces mathematical concepts with minimum prerequisites
- Uses concepts to derive four central machine learning methods
- Includes worked examples and exercises in every chapter
- Programming tutorials offered on book's website
- Suitable for students with mathematical background or those learning the mathematics for the first time
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