Linear algebra is perhaps the most important branch of mathematics for computational sciences, including machine learning, AI, data science, statistics, simulations, computer graphics, multivariate analyses, matrix decompositions, signal processing, and so on.
The way linear algebra is presented in traditional textbooks is different from how professionals use linear algebra in computers to solve real-world applications in machine learning, data science, statistics, and signal processing. For example, the "determinant" of a matrix is important for linear algebra theory, but should you actually use the determinant in practical applications? The answer may surprise you
If you are interested in learning the mathematical concepts linear algebra and matrix analysis, but also want to apply those concepts to data analyses on computers (e.g., statistics or signal processing), then this book is for you. You'll see all the math concepts implemented in MATLAB and in Python.

Unique aspects of this book:
- Clear and comprehensible explanations of concepts and theories in linear algebra.
- Several distinct explanations of the same ideas, which is a proven technique for learning.
- Visualization using graphs, which strengthens the geometric intuition of linear algebra.
- Implementations in MATLAB and Python. Com'on, in the real world, you never solve math problems by hand You need to know how to implement math in software
- Beginner to intermediate topics, including vectors, matrix multiplications, least-squares projections, eigendecomposition, and singular-value decomposition.
- Strong focus on modern applications-oriented aspects of linear algebra and matrix analysis.
- Intuitive visual explanations of diagonalization, eigenvalues and eigenvectors, and singular value decomposition.
- Codes (MATLAB and Python) are provided to help you understand and apply linear algebra concepts on computers.
- A combination of hand-solved exercises and more advanced code challenges. Math is not a spectator sport