Linear Algebra Courses and Certifications

Mathematics for Machine Learning: Linear Algebra

9 ratings

Builds intuition for linear algebra through short Python notebooks and applications such as image transformations and PageRank.

• Coursera
• 5 weeks, 4 hours a week
• Self-Paced
• Paid Course

Matrix Algebra for Engineers

905 ratings

This course is all about matrices, and concisely covers the linear algebra that an engineer should know. We define matrices and how to add and multiply them, and introduce some special types of matrices.

• Coursera
• 4 weeks, 5 hours a week
• Self-Paced
• Paid Course

• Best of All Time

Linear Algebra - Foundations to Frontiers

15 ratings

Learn the mathematics behind linear algebra and link it to matrix software development.

• edX
• 15 weeks, 6-10 hours a week
• Self-Paced
• Free Online Course (Audit)

Linear Algebra

8 ratings

This course covers matrix theory and linear algebra, emphasizing topics useful in other disciplines such as physics, economics and social sciences, natural sciences, and engineering.

• MIT OpenCourseWare
• 36 hours
• Self-Paced
• Free Video

Linear Algebra for Data Science in R

143 ratings at DataCamp

Linear algebra is one of the most important set of tools in applied mathematics and data science.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Linear Algebra Essentials

An introductory, visual and computational tour of vectors, linear combinations, transformations, and matrices, with a glimpse of their role in neural networks.

• Udacity
• 9 hours
• Self-Paced
• Paid Course

Linear Algebra Refresher Course

Learn linear algebra by doing: you will code your own library of linear algebra functions!

• Udacity
• 7 hours
• Self-Paced
• Free Online Course

Introduction to Linear Algebra for Machine Learning

Learn the linear algebra foundations—vectors, matrices, multiplication, and determinants—used to build and optimize machine learning models.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate

Career Certificate

Linear Algebra Programming with NumPy

Learn core linear algebra concepts—vectors, matrices, systems, and eigenvalues—through practical NumPy exercises for data science and machine learning.

• CodeSignal
• 10 hours
• Self-Paced
• Free Certificate
• 3 courses

Career Certificate

Linear Algebra Programming with R

Apply vectors, matrices, and eigenvalue methods in R to data science and statistical analysis.

• CodeSignal
• 10 hours
• Self-Paced
• Free Certificate
• 3 courses

Linear Algebra for Data Science: Practical Applications with Python

Jump in this 20 Hours course to master essential linear algebra techniques for turning raw data into actionable insights using Python and to elevate your analytical and computational skills to attain proficiency in high-impact data solutions with real-wo…

• OpenLearning
• 20 hours
• Self-Paced
• Paid Course

Linear Algebra for Machine Learning and Data Science

2379 ratings at Coursera

Represent data as vectors and matrices: solve linear systems by elimination, work with rank, inverses, linear transformations, determinants, and eigenvectors through Python lab exercises.

• Coursera
• 4 weeks, 9 hours a week
• Self-Paced
• Paid Course

Career Certificate

Linear Algebra from Elementary to Advanced

242 ratings at Coursera

Learn undergraduate linear algebra from linear systems and matrix equations through determinants, eigenvectors, orthogonality, diagonalization, symmetric matrices and quadratic forms, with equal algebraic and geometric emphasis.

• Coursera
• 4 weeks, 10 hours a week
• Self-Paced
• Paid Course
• 3 courses

Linear Algebra: Linear Systems and Matrix Equations

181 ratings at Coursera

Solve and classify linear systems with the row reduction algorithm, then connect matrix equations, vectors, linear independence and linear transformations while reading and writing mathematical proofs.

• Coursera
• 4 weeks, 3 hours a week
• Self-Paced
• Paid Course

Linear Algebra: Matrix Algebra, Determinants, & Eigenvectors

79 ratings at Coursera

Manipulate matrices as compositions of linear transformations, test invertibility, identify subspaces and dimension in R^n, compute determinants, and diagonalize matrices using eigenvalues and eigenvectors.

• Coursera
• 6 weeks, 2 hours a week
• Self-Paced
• Paid Course