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Item #:
112607187
Linear Algebra for Data Science, Machine Learning, and Signal Processing Hardcover – 16 May 2024
Item #:
112607187
€ 72
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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
| Item Weight | 2 lbs (910 grams) |
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.
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Casual Learners
Individuals seeking a light introduction to linear algebra might find this textbook too dense and technical.
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Non-Technical Readers
Readers without a technical background may find the content difficult to understand and apply in practical terms.
Product Description
Linear Algebra for Data Science, Machine Learning, and Signal Processing Hardcover – 16 May 2024
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Customer Questions & Answers
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Question:
Who is this textbook intended for?
Answer: It is perfect for upper-level undergraduates and first-year graduate students with prior linear algebra knowledge. -
Question:
Does the book include exercises for students?
Answer: Yes, it includes over 200 multiple-choice questions and homework exercises with solutions. -
Question:
Are there practical applications included in the book?
Answer: Absolutely, it highlights various applications such as low-rank approximation, logistic regression, and dimensionality reduction.
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Pros
- Comprehensive coverage of topics
- Accessible to beginners
- Clear explanations and examples
- Useful for various applications
- Well-structured and organized
Cons
- Some advanced topics may require further study.
Product Price History
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€ 72
Order now and get it around Tuesday, June 30
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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.