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Item #:
112607187
Linear Algebra for Data Science, Machine Learning, and Signal Processing
86% of respondents would recommend this to a friend
Item #:
112607187
PYG 637867
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Maximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package.
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What Stands Out
Comprehensive Coverage
Offers a thorough exploration of linear algebra concepts, tailored specifically for applications in data science, machine learning, and signal processing, ensuring a deep understanding for learners.
Practical Applications
Focuses on real-world applications and problem-solving techniques, enabling readers to effectively apply linear algebra techniques in their projects and research across various emerging fields.
Expertly Crafted
Written by industry experts and academics, this resource provides a blend of theory and practice, equipping readers with the high-quality knowledge necessary to excel in modern analytical environments.
Product Details
| Publisher | Cambridge University Press |
| Publication date | 16 May 2024 |
| Edition | 1st |
| Language | English |
| Print length | 450 pages |
| ISBN-10 | 1009418149 |
| ISBN-13 | 978-1009418140 |
| Item weight | 930 g |
| Dimensions | 17.15 x 3.18 x 24.13 cm |
Who Should Buy?
-
Data Science Students
Students pursuing data science will benefit from foundational linear algebra skills necessary for data analysis and modeling.
-
Machine Learning Practitioners
Practitioners looking to deepen their understanding of machine learning algorithms will find valuable insights in this text.
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Signal Processing Engineers
Engineers working in signal processing will appreciate the practical applications of linear algebra in their field.
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Beginners in Mathematics
Complete novices in mathematics may struggle with the complex concepts presented without prior foundational knowledge.
Product Description
Linear Algebra for Data Science, Machine Learning, and Signal Processing
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Telecommunications Editorial Review
Linear Algebra for Data Science, Machine Learning, and Signal Processing offers a comprehensive and essential resource for students and professionals alike. Published by Cambridge University Press, this first edition spans 450 pages, making it a substantial reference on applying linear algebra concepts in practical fields such as machine learning and signal processing. Readers have appreciated its clarity and structured approach, which allows for a smooth learning curve even for complex topics. The book is meticulously designed with a focus on real-world applications, and its engaging writing style enhances the reader's understanding. Its balanced coverage of theory and practice has made it a favorite among learners and educators in data science, illustrating the relevance of linear algebra in modern computing.
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Pros
- Comprehensive coverage of linear algebra applications
- Easy-to-understand explanations for complex topics
- Includes practical examples from data science
- Well-structured learning progression for readers
- Engaging writing style enhances understanding
Cons
- The print length may feel extensive for some readers
Product Price History
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Features & Benefits
- Engaging textbook designed for upper-level undergraduates and first-year graduate students.
- Covers both basic and advanced matrix topics relevant to data science and machine learning.
- Includes 200+ multiple-choice questions for interactive classroom learning.
- Features practical exercises with solutions available for instructors.
- Encourages active learning through 'explore' questions and practical Julia code examples.
- Offers a suite of computational notebooks for hands-on learning experiences.
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