Linear algebra is one of the key tools in data analysis and machine learning. Without understanding it, it is impossible to work effectively with large volumes of information, build models, and optimize computations. The book "Mathematics for Data Science: Linear Algebra with Matlab" by César Pérez López offers a detailed breakdown of linear algebra concepts essential for data science professionals. The focus is on practical application of knowledge using Matlab — a powerful tool for numerical computing.
Download the "Mathematics for Data Science: Linear Algebra with Matlab" book in PDF to master vital concepts, with Matlab usage accelerating the transition from theory to practice.
What will you learn by reading this guide?
- Fundamental principles of linear algebra and their connection to data analysis
- How to use matrices and vectors for data processing
- Matrix decomposition methods used in machine learning
- Practical applications of linear algebra in Matlab
- Introduction to eigenvalues and eigenvectors for analyzing complex systems
- Working with optimization and regression in the context of data analysis
Who should read "Mathematics for Data Science: Linear Algebra with Matlab"?
- Aspiring data science professionals: If you're just getting started in Data Science, this book will help you grasp the essential mathematics needed to work with algorithms effectively
- Developers and engineers working with numerical computations: If your work involves data processing, modeling, or machine learning, this book provides a strong mathematical foundation
- Students in technical fields: Many data analysis and machine learning courses require linear algebra. This edition helps systematize your knowledge
- Programmers working with Matlab: If you use Matlab for numerical modeling and data processing, the book shows how to apply linear algebra to real-world problems
The Developer's Opinion About the Book
This book teaches the essential math behind data science: probability, statistics, linear algebra, and calculus. Each topic is explained with clarity and tied to ML applications. After reading, you’ll better understand model behavior and assumptions. Suitable for anyone who wants to go beyond black-box usage. It bridges intuition and application, helping readers derive formulas used in libraries like scikit-learn or TensorFlow.
Jason Nguyen, Data Scientist
FAQ for "Mathematics for Data Science: Linear Algebra with Matlab"
Can the knowledge from this book be applied in Python?
Yes, although it's focused on Matlab, all the mathematical concepts are universal. They can be easily adapted to Python using libraries like NumPy, SciPy, and Pandas.
How difficult is the material?
The book is designed for programmers with basic math knowledge. If you understand vectors and matrices, the material will be easy to follow.
Is this textbook suitable for preparing to work with neural networks?
Yes, since linear algebra forms the foundation of deep learning and multidimensional data processing, understanding these concepts will accelerate your work with neural networks.
Will this book be useful for those already familiar with linear algebra?
If you've already worked with matrices, decomposition, and eigenvalues, the book will help structure your knowledge and teach you how to apply it in Matlab.
Does the book include real-world examples of linear algebra in data analysis?
Yes, César Pérez López discusses practical tasks including big data, regression, and image processing.
Is Matlab experience necessary?
Basic Matlab knowledge is recommended. However, the author explains all the key functions, so even a beginner can understand the material.
Information
| Author: | César Pérez López | Language: | English |
| Publisher: | - | ISBN-13: | - |
| Publication Date: | 2025 | ISBN-10: | - |
| Print Length: | 390 pages | Category: | Data Science Books |
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