Linear algebra courses cover key concepts such as vectors, matrices, determinants, and linear transformations. You will learn how to solve systems of equations, perform operations like matrix multiplication, and understand eigenvalues and eigenvectors. These skills are applicable in various fields, including computer graphics, data science, and optimization.

Johns Hopkins University
Beginner · Specialization

Imperial College London
Skills you'll gain: Linear Algebra, Applied Mathematics, Data Science, Data Manipulation, Data Transformation, Machine Learning
★ 4.6 (13K) · Beginner · Course · 1 - 3 Months

The University of Sydney
Skills you'll gain: Linear Algebra, Markov Model, Geometry, Arithmetic, Algebra, Advanced Mathematics, Applied Mathematics
★ 4.8 (48) · Intermediate · Course · 1 - 4 Weeks

DeepLearning.AI
Skills you'll gain: Linear Algebra, Dimensionality Reduction, Applied Mathematics, Machine Learning, Python Programming, Algebra
★ 4.6 (2.4K) · Intermediate · Course · 1 - 4 Weeks

Johns Hopkins University
Skills you'll gain: Linear Algebra, Geometry
★ 4.7 (183) · Beginner · Course · 1 - 4 Weeks

Imperial College London
Skills you'll gain: Dimensionality Reduction, Linear Algebra, Regression Analysis, NumPy, Calculus, Applied Mathematics, Descriptive Statistics, Model Optimization, Statistics, Geometry, Artificial Neural Networks, Data Science, Data Manipulation, Python Programming, Data Transformation, Machine Learning, Derivatives
★ 4.6 (15K) · Beginner · Specialization · 3 - 6 Months

The Hong Kong University of Science and Technology
Skills you'll gain: Linear Algebra, Algebra, Applied Mathematics
★ 4.9 (4.7K) · Beginner · Course · 1 - 4 Weeks

Birla Institute of Technology & Science, Pilani
Skills you'll gain: Linear Algebra, Numerical Analysis, Artificial Intelligence and Machine Learning (AI/ML), Applied Mathematics, Data Analysis, Machine Learning, Dimensionality Reduction, Model Optimization, Artificial Neural Networks
Beginner · Course · 1 - 3 Months

Johns Hopkins University
Skills you'll gain: Linear Algebra, Applied Mathematics, Algebra, Geometry, Markov Model
★ 4.8 (79) · Mixed · Course · 1 - 3 Months

DeepLearning.AI
Skills you'll gain: Descriptive Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Probability & Statistics, Sampling (Statistics), Statistical Methods, Probability Distribution, Linear Algebra, Statistical Inference, Statistics, A/B Testing, Statistical Analysis, Applied Mathematics, Probability, Calculus, Dimensionality Reduction, Exploratory Data Analysis, Machine Learning, Python Programming, Artificial Neural Networks
★ 4.6 (3.3K) · Intermediate · Specialization · 1 - 3 Months

Johns Hopkins University
Skills you'll gain: Algebra, Mathematical Modeling, Graphing, Advanced Mathematics, Applied Mathematics, Deductive Reasoning, Analytical Skills, Arithmetic, Probability & Statistics, Geometry
★ 4.8 (824) · Beginner · Specialization · 3 - 6 Months

The Hong Kong University of Science and Technology
Skills you'll gain: Differential Equations, Linear Algebra, Matlab, Numerical Analysis, Integral Calculus, Calculus, electromagnetics, Algebra, Applied Mathematics, Geometry, Mechanics, Scripting, Simulations, Physics, Plot (Graphics), Derivatives
★ 4.8 (7.9K) · Beginner · Specialization · 3 - 6 Months
Start with your goal for linear algebra. If you want to apply it in machine learning, choose courses that cover matrix operations, eigenvalues, and their applications in algorithms. If you’re focused on the theoretical side, look for courses that emphasize proofs, vector spaces, and linear transformations.
Top-rated Linear Algebra courses offered by Johns Hopkins University on Coursera.
Earn a certificate in Linear Algebra from top universities and companies.
Top-rated beginner-friendly Linear Algebra courses with no prerequisites.
Linear algebra is a branch of mathematics that deals with vectors, vector spaces, and linear transformations. It is essential for various fields, including engineering, physics, computer science, and economics. Understanding linear algebra is crucial because it provides the foundational tools for solving systems of linear equations, which are prevalent in real-world applications such as data analysis, machine learning, and optimization problems.‎
Jobs that require knowledge of linear algebra are diverse and span multiple industries. Positions such as data analyst, machine learning engineer, software developer, and quantitative researcher often demand a solid understanding of linear algebra concepts. Additionally, roles in finance, engineering, and academia may also benefit from linear algebra skills, as they are frequently used in modeling and problem-solving.‎
To learn linear algebra effectively, you should focus on developing several key skills. These include understanding vector and matrix operations, grasping concepts of vector spaces, and being able to solve linear equations. Familiarity with mathematical proofs and the ability to apply linear algebra concepts to real-world problems, particularly in data science and machine learning, will also enhance your learning experience.‎
Some of the best online courses for learning linear algebra include the Introduction to Linear Algebra and the Linear Algebra for Data Science Using Python Specialization. These courses cater to various skill levels and provide practical applications of linear algebra concepts, making them suitable for both beginners and those looking to deepen their knowledge.‎
Yes. You can start learning linear algebra on Coursera for free in two ways:
If you want to keep learning, earn a certificate in linear algebra, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎
To learn linear algebra, start by exploring online courses that provide structured content and practical exercises. Engage with interactive materials, such as videos and quizzes, to reinforce your understanding. Additionally, practice solving problems and applying concepts to real-world scenarios. Joining study groups or forums can also enhance your learning experience by allowing you to discuss concepts with peers.‎
Typical topics covered in linear algebra courses include vector operations, matrix algebra, determinants, eigenvalues, and eigenvectors. Courses may also explore applications of linear algebra in data science, such as regression analysis and dimensionality reduction. Understanding these topics will provide you with a comprehensive foundation in linear algebra and its practical uses.‎
For training and upskilling employees, courses like the Linear Algebra for Machine Learning & AI and Linear Algebra and Regression Fundamentals for Data Science are excellent choices. These courses focus on practical applications of linear algebra in data-driven environments, making them suitable for workforce development.‎