Data Science

10 useful linear algebra resource for machine learning

Linear Algebra

Machine learning is all about linear algebra. It is very hard to find a good resource to get hold of the subject. Here we made a list of 10 useful resources to learn linear algebra for machine learning.

3 Blue 1 Brown

3 blue 1 brown or 3b1b is a youtube channel that focuses on the visual animated representation of mathematical concepts. Sanderson the author of the channel is graduated from Standford and has a very unique way to convey concepts. All projects start with particular illustrations. The topics covered in this channel are in the form of a storyline.

Click here to go to the channel.

Khan Academy

Khan academy is a nonprofit educational site whose sole purpose is to make sure that everyone has access to free education around the globe. Khan academy offers classwise courses from kindergarten to high school. They also have partnered with institutes like NASA, MIT, and others to provide students with specialized content. It is the best resource to learn Linear Algebra for machine learning with multiple quiz exercises to polish your skills.

Goto khan academy.

It’s a free online course by is a company that provides resources for free education regarding deep learning. Numerical methods for linear algebra are the subject of the course. It is an application of matrix algebra on computers and discusses all the questions about efficiency and precision. Python with NumPy, sci-kit-learn, numba, PyTorch, etc. is used with examples in the course. The content is taught with a top-down approach to offer a sense of how the methods work before they are explained.

Click here to visit the site.


Massachusetts Institute of Technology (MIT) is considered to be the worlds top-notch institute. Worlds top researchers teach students at MIT. Studying at MIT is like a dream come true. However, it is too expensive for anybody to afford an education at MIT, but not impossible. You can learn subjects of your choice from the MIT open courseware program. You can find video lectures, course notes, and much more in MIT open courseware. The same goes for Linear Algebra, which is taught by Prof. Gilbert Strang at MIT.

Follow MIT OpenCourseWare program here.

Introduction to linear algebra – Gilbert Strang

Prof. Gilbert Strang is a mathematics professor back at MIT and among the first faculty members to publish courses at MIT OpenCourseWare. Prof. Gilbert is a very passionate professor of mathematics and has many publications in the field. The video lectures of the professor are provided in the MIT sections above. However, the professor has written books on Linear algebra. You can follow an introduction to linear algebra, to begin with, the basics maths for Machine Learning.

Download book here.

Lloyd Trefethen

Lloyd Trefethen is head of the Mathematical Institute Number Analysis Group at Oxford University. This is a descriptive and detailed introduction to the field of linear numerical algebra. The clarity and eloquence of the lecture make it popular with both teachers and students. The text aims to expand readers’ comprehension of the field and present traditional content in a new way.

Get on Google Books or Amazon.

Linear ALgebra and its applications – Gilbert Strang

Another book by Prof. Gilbert Strang, linear algebra and its applications, an excellent resource to get in-depth detail about matrices, vectors, and the operations performed on them. He also discussed linear programming and game theory and how to map those concepts practically.

Download the book here.

Gene Golub and Charles Van Loan

Gene H. Golub is a Stanford University Professor and Charles F. Van is Cornell University Professor of Computer Science. The book gives important information on the maths and algorithmic knowledge required in numerical software production.

Get on Google Books or Amazon.

Linear Algebra: Foundations to frontiers – edx

Edx is a well-known platform for online education. Many courses are offered there from which one can benefit. Linear Algebra Foundations to frontiers is a good course to start with Linear algebra for machine learning. It offers a wide variety of resources and includes large data sets to work on. It is very popular among students and teachers as It’s visual explanations help students to understand the concepts. It provides hands-on exposure to calculations, mathematical abstractions, and computer programming. It helps you to lean the development of mathematical theory.

Course Link.

Andrew Ng’s Machine Learning course – Coursera

Andrew Ng is considered a renowned professor of Machine Learning and Deep Learning at Standford University. He has extensive work in the field. Andrew Ng offers a free course at Coursera in which he teaches Machine Learning and as well as basic Algebraic skills required for the task. It is very much recommended to take this course.

Course Link.

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