9 Best Linear Algebra Courses for Data Science & Machine Learning in 2024

In data science, Linear algebra is used in data preprocessing, data transformation, dimensionality reduction, and model evaluation. That’s why in this article, I am gonna share the 9 Best Linear Algebra Courses for Data Science and Machine Learning. So give your few minutes and find out some best resources to learn linear algebra for data science and machine learning.

In machine learning, most of the time we deal with scalars and vectors, and matrices. For example in logistic regression, we do vector-matrix multiplication. Sometimes we do clustering of input by using spectral clustering techniques, and for this, we need to know eigenvalues and eigenvectors.

Before I discuss the Linear Algebra Courses, I would like to mention what topics in linear algebra you need to learn for data science and machine learning-

Topics to Learn in Linear Algebra-

• Basic properties of a matrix and vectors: scalar multiplication, linear transformation, transpose, conjugate, rank, and determinant.
• Inner and outer products, matrix multiplication rule and various algorithms, and matrix inverse.
• Matrix factorization concept/LU decomposition, Gaussian/Gauss-Jordan elimination, solving Ax=b linear system of an equation.
• Eigenvalues, eigenvectors, diagonalization, and singular value decomposition.
• Special matrices: square matrix, identity matrix, triangular matrix, the idea about sparse and dense matrix, unit vectors, symmetric matrix, Hermitian, skew-Hermitian and unitary matrices.
• Vector space, basis, span, orthogonality, orthonormality, and linear least square.

Now without any further ado, let’s start finding the Best Linear Algebra Courses for Data Science and Machine Learning.

Best Linear Algebra Courses for Data Science and Machine Learning

1. Linear Algebra Refresher Course– Udacity

Time to Complete- 4 Months

This is a Free refresher course to learn the basics of linear algebra. In this course, you will learn the basic operations of vectors and the geometric and algebraic interpretation of intersections of “flat” objects.

You will also learn how to write your own algorithm to find the intersections of sets of lines and planes. After completing this course, you will have coded your own personal library of linear algebra functions that you can use to solve real-world problems.

You Should Enroll if-

• You have experience with some programming language.

Interested to Enroll?

If yes, then start learning- Linear Algebra Refresher Course

2. Mathematics for Machine Learning: Linear Algebra– Coursera

Rating- 4.7/5

Provider- Imperial College London

Time to Complete- 19 hours

This course is the part of Mathematics for Machine Learning Specialization program. This is the best course to refresh your linear algebra skills. In this course, you will learn about vectors and matrices and how to use them with datasets for performing some funny stuff such as rotation of face images, etc.

Along with learning theoretical concepts, you will also implement them by writing code in python. Now let’s see the syllabus of the course-

Syllabus of the Course-

1. Introduction to Linear Algebra and to Mathematics for Machine Learning
2. Vectors are objects that move around space
3. Matrices in Linear Algebra: Objects that operate on Vectors
4. Matrices make linear mappings
5. Eigenvalues and Eigenvectors: Application to Data Problems

Extra Benefits-

1. You will get a Shareable Certificate upon completion.
2. Along with this, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, Graded Programming Assignments.

Who Should Enroll?

• Those who have studied high school linear algebra.

Interested to Enroll?

If yes, then check out all details here- Mathematics for Machine Learning: Linear Algebra

3. The Math of Data Science: Linear Algebra– edX

Provider- RICE University

Time to Complete- 8 Weeks( If you spend 6-8 hours per week)

This is another best course to learn Linear algebra for data science. In this course, you will learn relationships between linear equations, matrices, and linear transformations, the significance of the basis and dimension of a vector space, etc.

This course will not only teach theoretical concepts but also teach how to use linear algebra to solve real-world problems. This Linear Algebra course also covers some advanced concepts of linear algebra such as basis and dimension.

Extra Benefits-

• You will get a Shareable Certificate upon completion.

Who Should Enroll?

• Those who studied High school algebra.

Interested to Enroll?

If yes, then check out all details here- The Math of Data Science: Linear Algebra

4. Learn Linear Algebra-Khan Academy

Rating- 4.4/5

Khan Academy course is good for those who want to brush up on their linear algebra basics. In this course, you will learn Vectors, Matrix transformation, Alternate coordinate systems, etc.

This course also covers linear combinations and spans, vector dot and cross products, null space and shared space, linear dependence, and independence, etc.

Who Should Enroll?

• Who are beginners and want to brush up on their linear algebra skills.

Interested to Enroll?

If yes, then check out all details here- Learn Linear Algebra

5. First Steps in Linear Algebra for Machine Learning– Coursera

Rating- 4.1/5

Provider- National Research University Higher School of Economics

Time to Complete- 14 hours

This is the best course for learning linear algebra for data analysis and machine learning. This course is part of Mathematics for Data Science Specialization.

In this course, you will learn the fundamentals of working with data in vector and matrix form. Now let’s see the syllabus of the course-

Syllabus of the Course-

1. Systems of linear equations and linear classifier
2. Full rank decomposition and systems of linear equations
3. Euclidean spaces
4. Final Project

Extra Benefits-

1. You will get a Shareable Certificate upon completion.
2. Along with this, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, Graded Programming Assignments.

Who Should Enroll?

• Those who are familiar with Python Programming and basic algebra.

Interested to Enroll?

If yes, then check out all details here- First Steps in Linear Algebra for Machine Learning

6. Linear Algebra – Foundations to Frontiers– edX

Provider- UTAustinX

Time to Complete- 15 Weeks( If you spend 6-10 hours per week)

This is another best course to learn linear algebra concepts. In this course, you will learn Vector and Matrix Operations, Linear Transformations, Solving Systems of Equations, Vector Spaces, Linear Least-Squares, and Eigenvalues and Eigenvectors, etc.

Along with this, you will also get to know about the research on the development of linear algebra libraries. This course was taught by Professor Robert van de Geijn, an expert on high-performance linear algebra libraries.

Extra Benefits-

• You will get a Shareable Certificate upon completion.
• You will get a MATLAB license throughout this course free of charge.

Who Should Enroll?

• Who studied High School Algebra, Geometry, and Pre-Calculus.

Interested to Enroll?

If yes, then check out all details here- Linear Algebra – Foundations to Frontiers

7. Linear Algebra for Data Science in R-Datacamp

Time to Complete- 4 hours

In this course, you will learn Linear Algebra basics such as vectors and matrices, eigenvalue and eigenvector analyses, etc. You will also learn how to perform dimension reduction on real-world datasets by using principal component analysis.

This course use the R programming language for performing all analysis. There are 4 chapters in this course-

1. Introduction to Linear Algebra
2. Matrix-Vector Equations
3. Eigenvalues and Eigenvectors
4. Principal Component Analysis

Who Should Enroll?

• Those who know R programming language.

Interested to Enroll?

If yes, then check out the course details here- Linear Algebra for Data Science in R

8. Become a Linear Algebra Master– Udemy

Rating- 4.7/5

Provider- Krista King

Time to Complete- 15 hours

This linear algebra course is not dedicated to data science learners but covers all required linear algebra topics for data science. In this course, you will learn Matrices as vectors, Matrix-vector products, Inverses, Transposes, etc.

You will also get 69 quizzes with solutions and 12 workbooks for extra practice.

Extra Benefits-

• You will get a Certificate of completion.
• Along with this, you will get 171 downloadable resources and 98 articles.

Who Should Enroll?

• Those who know linear algebra basics.

Interested to Enroll?

If yes, then check out all details here- Become a Linear Algebra Master

9. Matrix Algebra for Engineers– Coursera

Rating- 4.8/5

Provider- The Hong Kong University of Science and Technology

Time to Complete- 20 hours

This course is taught by Jeffrey R. Chasnov, a Professor of Mathematics at the Hong Kong University of Science and Technology. In this course, you will learn matrices, a system of linear equations, vector spaces, eigenvalues, and eigenvectors.

In this course, all the concepts and techniques are clearly explained and there are various exercises for each lesson. If you are from an engineering background, then I would recommend you to take this course.

Extra Benefits-

1. You will get a Shareable Certificate upon completion.
2. Along with this, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, Graded Programming Assignments.

Who Should Enroll?

• Those who have a basic understanding of mathematics.

Interested to Enroll?

If yes, then check out all details here- Matrix Algebra for Engineers

And here the list ends. I hope these Best Linear Algebra Courses for Data Science and Machine Learning will definitely help you to learn linear algebra at your own pace. I would suggest you bookmark this article for future referrals. Now it’s time to wrap up.

Conclusion

In this article, I tried to cover all the Best Linear Algebra Courses for Data Science and Machine Learning. If you have any doubts or questions, feel free to ask me in the comment section.

All the Best!

Enjoy Learning!

Thank YOU!

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