Linear Algebra for Data Science & Machine Learning A-Z 2025

Linear Algebra for Data Science, Big Data, Machine Learning, Engineering & Computer Science. Master Linear Algebra

4.67 (790 reviews)

Udemy

platform

English

language

Career Development

What you will learn

Fundamentals of Linear Algebra and how to ace your Linear Algebra exam

Basics of matrices (notation, dimensions, types, addressing the entries etc.)

Operations on a single matrix, e.g. scalar multiplication, transpose, determinant & adjoint

Operations on two matrices, including addition, subtraction and multiplication of matrices

Performing elementary row operations and finding Echelon Forms (REF & RREF)

Inverses, including invertible and singular matrices, and the Cofactor method

Solving systems of linear equations using matrices and inverse matrices, including Cramer’s rule to solve AX = B

Properties of determinants, and how to perform Gauss-Jordan elimination

Matrices as vectors, including vector addition and subtraction, Head-to-Tail rule, components, magnitude and midpoint of a vector

Vector spaces, including dimensions, Euclidean spaces, closure properties and axioms

Linear combinations and span, spanning set for a vector space and linear dependence

Subspace and Null-space of a matrix, matrix-vector products

Basis and standard basis, and checking if a set of given vectors forms the basis for a vector space

Eigenvalues and Eigenvectors, including how to find Eigenvalues and the corresponding Eigenvectors

Basic algebra concepts ( as a BONUS)

And so much more…..

Course Gallery

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Rating & Reviews

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Comidoc Review

Our Verdict

Linear Algebra for Data Science & Machine Learning A-Z 2025 presents fundamental linear algebra concepts but lacks practical applications, real-life examples, and sufficient exercise material. The course might serve as a refresher or an introduction for beginners, but it may disappoint advanced learners seeking in-depth understanding of eigenvalues, Eigendecomposition, PCA, and how linear algebra connects to data science and machine learning.

What We Liked

Covers fundamental linear algebra concepts for data science
Gentle introduction to core linear algebra topics
Slow pace with lots of examples
Instructor explains how each step works and why operations are performed

Potential Drawbacks

Missing real-life examples and context
Lacks practical applications in data science and machine learning
Limited number of exercises and assignments
Some content is too brief and difficult to understand