• For Individuals
  • For Businesses
  • For Universities
  • For Governments
Coursera
  • Coursera Plus
  • Log In
  • Join for Free
    Coursera
    • Browse
    • Matrix Algebra

    Matrix Algebra Courses Online

    Master matrix algebra for solving linear equations and transformations. Learn about matrix operations, vector spaces, and applications in science and engineering.

    Skip to search results

    Filter by

    Subject
    Required
     *

    Language
    Required
     *

    The language used throughout the course, in both instruction and assessments.

    Learning Product
    Required
     *

    Build job-relevant skills in under 2 hours with hands-on tutorials.
    Learn from top instructors with graded assignments, videos, and discussion forums.
    Learn a new tool or skill in an interactive, hands-on environment.
    Get in-depth knowledge of a subject by completing a series of courses and projects.
    Earn career credentials from industry leaders that demonstrate your expertise.
    Earn career credentials while taking courses that count towards your Master’s degree.
    Earn your Bachelor’s or Master’s degree online for a fraction of the cost of in-person learning.
    Earn a university-issued career credential in a flexible, interactive format.

    Level
    Required
     *

    Duration
    Required
     *

    Skills
    Required
     *

    Subtitles
    Required
     *

    Educator
    Required
     *

    Explore the Matrix Algebra Course Catalog

    • T

      The Hong Kong University of Science and Technology

      Matrix Algebra for Engineers

      Skills you'll gain: Linear Algebra, Engineering Calculations, Algebra, Engineering Analysis, General Mathematics, Advanced Mathematics, Applied Mathematics

      4.9
      Rating, 4.9 out of 5 stars
      ·
      4.5K reviews

      Beginner · Course · 1 - 4 Weeks

    • I

      Imperial College London

      Mathematics for Machine Learning

      Skills you'll gain: Linear Algebra, Dimensionality Reduction, NumPy, Regression Analysis, Calculus, Applied Mathematics, Probability & Statistics, Feature Engineering, Jupyter, Advanced Mathematics, Data Science, Statistics, Machine Learning Algorithms, Machine Learning Methods, Statistical Analysis, Artificial Neural Networks, Algorithms, Data Manipulation, Python Programming, Machine Learning

      4.6
      Rating, 4.6 out of 5 stars
      ·
      15K reviews

      Beginner · Specialization · 3 - 6 Months

    • D

      DeepLearning.AI

      Mathematics for Machine Learning and Data Science

      Skills you'll gain: Descriptive Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Probability & Statistics, Sampling (Statistics), Probability Distribution, Probability, Linear Algebra, Statistical Inference, A/B Testing, Applied Mathematics, NumPy, Calculus, Dimensionality Reduction, Mathematical Modeling, Machine Learning, Machine Learning Methods, Python Programming, Jupyter, Data Manipulation

      4.6
      Rating, 4.6 out of 5 stars
      ·
      2.7K reviews

      Intermediate · Specialization · 1 - 3 Months

    • Unlock Access to 10,000+ courses with a subscription.

      Learn more
    • J

      Johns Hopkins University

      Linear Algebra: Matrix Algebra, Determinants, & Eigenvectors

      Skills you'll gain: Linear Algebra, Applied Mathematics, Algebra, Advanced Mathematics, Graph Theory, Geometry, Applied Machine Learning, Markov Model, Probability

      4.8
      Rating, 4.8 out of 5 stars
      ·
      57 reviews

      Mixed · Course · 1 - 3 Months

    • I

      Imperial College London

      Statistical Analysis with R for Public Health

      Skills you'll gain: Analytical Skills, Correlation Analysis, Regression Analysis, Sampling (Statistics), Statistical Hypothesis Testing, Data Literacy, Data Analysis, R Programming, Descriptive Statistics, Statistical Software, Biostatistics, Exploratory Data Analysis, Statistical Analysis, Statistical Programming, Statistics, Statistical Methods, Public Health, Probability & Statistics, Epidemiology, Statistical Modeling

      4.7
      Rating, 4.7 out of 5 stars
      ·
      2K reviews

      Beginner · Specialization · 3 - 6 Months

    What brings you to Coursera today?

    • I

      Imperial College London

      Mathematics for Machine Learning: Linear Algebra

      Skills you'll gain: Linear Algebra, NumPy, Applied Mathematics, Jupyter, Machine Learning Methods, Algorithms, Data Manipulation, Python Programming

      4.7
      Rating, 4.7 out of 5 stars
      ·
      12K reviews

      Beginner · Course · 1 - 3 Months

    • Status: Free
      Free
      U

      University of Minnesota

      Matrix Methods

      Skills you'll gain: Dimensionality Reduction, NumPy, Linear Algebra, Machine Learning Methods, Data Analysis, Numerical Analysis, Mathematical Modeling, Applied Mathematics, Applied Machine Learning, Data Manipulation, Exploratory Data Analysis, Algorithms, Python Programming

      4.1
      Rating, 4.1 out of 5 stars
      ·
      243 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free
      Free
      N

      National Taiwan University

      機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations

      Skills you'll gain: Supervised Learning, Machine Learning, Classification And Regression Tree (CART), Theoretical Computer Science, Applied Mathematics, Mathematical Modeling, Probability & Statistics, Regression Analysis, Algorithms

      4.9
      Rating, 4.9 out of 5 stars
      ·
      931 reviews

      Beginner · Course · 1 - 3 Months

    • T

      The Hong Kong University of Science and Technology

      Mathematics for Engineers

      Skills you'll gain: Differential Equations, Linear Algebra, Matlab, Engineering Calculations, Engineering Analysis, Numerical Analysis, Finite Element Methods, Integral Calculus, Mathematical Software, Mechanical Engineering, Calculus, Algebra, Applied Mathematics, Mathematical Modeling, Engineering, Simulation and Simulation Software, Advanced Mathematics, Geometry, Computational Thinking, Estimation

      4.8
      Rating, 4.8 out of 5 stars
      ·
      7.5K reviews

      Beginner · Specialization · 3 - 6 Months

    • J

      Johns Hopkins University

      Linear Algebra from Elementary to Advanced

      Skills you'll gain: Linear Algebra, Algebra, Applied Mathematics, Artificial Intelligence and Machine Learning (AI/ML), Mathematical Modeling, Advanced Mathematics, Engineering Analysis, Mathematical Theory & Analysis, Numerical Analysis, Geometry, Graph Theory, Applied Machine Learning, Markov Model, Probability

      4.7
      Rating, 4.7 out of 5 stars
      ·
      167 reviews

      Beginner · Specialization · 3 - 6 Months

    • J

      Johns Hopkins University

      Algebra: Elementary to Advanced

      Skills you'll gain: Algebra, Mathematical Modeling, Graphing, Arithmetic, Advanced Mathematics, Applied Mathematics, General Mathematics, Mathematical Theory & Analysis, Analytical Skills, Probability & Statistics, Geometry

      4.8
      Rating, 4.8 out of 5 stars
      ·
      723 reviews

      Beginner · Specialization · 3 - 6 Months

    • U

      University of California San Diego

      Data Structures and Algorithms

      Skills you'll gain: Data Structures, Graph Theory, Algorithms, Network Routing, Program Development, Debugging, Network Model, Bioinformatics, Operations Research, Data Storage, Development Testing, Test Engineering, Software Testing, Theoretical Computer Science, Computational Thinking, Network Analysis, Test Case, Programming Principles, Computer Programming, Epidemiology

      4.6
      Rating, 4.6 out of 5 stars
      ·
      17K reviews

      Intermediate · Specialization · 3 - 6 Months

    What brings you to Coursera today?

      Matrix Algebra learners also search

      Abstract Algebra
      Physics
      Quantum
      Quantum Physics
      Theoretical Computer Science
      Quantum Computing
      Calculus
      Mathematics
      1234…82

      In summary, here are 10 of our most popular matrix algebra courses

      • Matrix Algebra for Engineers: The Hong Kong University of Science and Technology
      • Mathematics for Machine Learning: Imperial College London
      • Mathematics for Machine Learning and Data Science: DeepLearning.AI
      • Linear Algebra: Matrix Algebra, Determinants, & Eigenvectors: Johns Hopkins University
      • Statistical Analysis with R for Public Health: Imperial College London
      • Mathematics for Machine Learning: Linear Algebra: Imperial College London
      • Matrix Methods: University of Minnesota
      • 機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations: National Taiwan University
      • Mathematics for Engineers: The Hong Kong University of Science and Technology
      • Linear Algebra from Elementary to Advanced: Johns Hopkins University

      Skills you can learn in Machine Learning

      Python Programming (33)
      Tensorflow (32)
      Deep Learning (30)
      Artificial Neural Network (24)
      Big Data (18)
      Statistical Classification (17)
      Reinforcement Learning (13)
      Algebra (10)
      Bayesian (10)
      Linear Algebra (10)
      Linear Regression (9)
      Numpy (9)

      Frequently Asked Questions about Matrix Algebra

      Matrix algebra is a branch of mathematics that deals with the manipulation and study of matrices. Matrices are rectangular arrays of numbers or symbols arranged in rows and columns. In matrix algebra, various operations such as addition, subtraction, multiplication, and division are performed on matrices to solve mathematical equations and systems of equations. This branch of algebra is widely used in many fields, including computer science, physics, engineering, and economics, as it provides a powerful tool for representing and solving complex mathematical problems.‎

      To effectively learn Matrix Algebra, you should focus on developing the following skills:

      1. Understanding Linear Algebra Basics: Before delving into Matrix Algebra, it is essential to have a strong foundation in linear algebra concepts such as vectors, vector spaces, and systems of linear equations.

      2. Matrix Operations: Familiarize yourself with matrix operations such as addition, subtraction, multiplication, and division. Also, learn about matrix properties like transpose, inverse, and determinant.

      3. Solving Systems of Linear Equations: Matrix Algebra is often used to solve systems of linear equations. Practice solving these systems using matrix methods like Gaussian elimination, LU decomposition, or inverse matrix techniques.

      4. Eigenvalues and Eigenvectors: Gain an understanding of eigenvalues and eigenvectors, which are crucial concepts in Matrix Algebra. These concepts are commonly used in various fields, including computer science, physics, and engineering.

      5. Matrix Decomposition: Learn about different matrix decomposition techniques like QR decomposition, Cholesky decomposition, and Singular Value Decomposition (SVD). These methods help in simplifying complex calculations and analyzing matrices.

      6. Matrix Calculus: Familiarize yourself with the basics of matrix calculus. The derivative and gradient of a function involving matrices are essential concepts to understand for advanced applications like optimization or machine learning algorithms.

      7. Real-world Applications: Apply Matrix Algebra to real-world problems and applications. This could include areas such as computer graphics, data analysis, machine learning, and network analysis.

      Remember, mastering Matrix Algebra takes time and practice. Utilize online resources, textbooks, video tutorials, and interactive courses to enhance your understanding and gain hands-on experience with matrix operations.‎

      Having skills in Matrix Algebra can qualify individuals for a wide range of job opportunities across various industries. Some potential job roles include:

      1. Data Scientist: Matrix Algebra is fundamental for data manipulation, statistical analysis, and advanced machine learning techniques. Data scientists use matrix operations to process and analyze large sets of data, uncover patterns and insights, and build predictive models.

      2. Financial Analyst: Financial institutions heavily rely on Matrix Algebra for portfolio management, risk assessment, and asset pricing. Financial analysts utilize matrix algebra to analyze financial data, create financial models, and assess investment opportunities.

      3. Actuary: Actuaries assess and manage risks in various industries, such as insurance, finance, and healthcare. Matrix Algebra is a vital tool for actuarial calculations, including determining insurance premiums, projecting financial risks, and assessing the impact of potential events or incidents.

      4. Software Engineer: Matrix Algebra is utilized in various fields of computer science, including computer graphics, cryptography, and machine learning. Software engineers use matrix operations for tasks like image and signal processing, data compression, and algorithm design.

      5. Operations Research Analyst: Matrix Algebra enables operations research analysts to model complex systems, optimize processes, and make data-driven decisions. They apply matrix operations to solve optimization problems, schedule resources, and improve operational efficiency across industries like logistics, transportation, and manufacturing.

      6. Aerospace Engineer: Matrix Algebra is extensively used in the field of aerospace engineering for tasks such as flight control, structural analysis, and trajectory calculations. Aerospace engineers utilize matrices to enhance navigation systems, analyze fluid dynamics, and design efficient propulsion systems.

      7. Robotics Engineer: Matrix Algebra plays a crucial role in robotics applications, including robotic control, kinematics, and computer vision. Robotics engineers use matrix operations to manipulate and analyze sensor data, calculate robot motion, and design autonomous systems.

      These are just a few examples of the diverse job opportunities available for individuals with Matrix Algebra skills. By acquiring these skills, you can set yourself up for a promising career in fields ranging from data science to engineering.‎

      Matrix Algebra is best suited for individuals who have a strong foundation in basic algebra and a keen interest in mathematics. It is particularly beneficial for students pursuing degrees in mathematics, physics, engineering, computer science, or any field that involves data analysis, modeling, or optimization. Additionally, individuals who enjoy problem-solving, critical thinking, and logical reasoning will find Matrix Algebra to be a valuable subject to study.‎

      There are several topics related to Matrix Algebra that you can study. Some of these include:

      1. Matrix Operations: This topic covers various operations on matrices such as addition, subtraction, multiplication, and division.

      2. Determinants: Determinants are used to determine whether a matrix has an inverse and also play a crucial role in linear transformation and eigenvalues.

      3. System of Linear Equations: The study of matrices is closely linked to solving systems of linear equations. You can learn about Gaussian elimination, Cramer's rule, and other methods for solving these systems.

      4. Vector Spaces: Matrix algebra is applied in the study of vector spaces, which involves analyzing the properties of vectors, subspaces, basis, and dimension.

      5. Orthogonality and Inner Product Spaces: This topic explores concepts of orthogonality, inner products, and orthogonal projection in the context of matrices.

      6. Eigenvalues and Eigenvectors: Eigenvalues and eigenvectors play a significant role in linear transformations, diagonalization, and solving differential equations.

      7. Singular Value Decomposition: It is a method used to decompose a matrix into simpler components, aiding in data compression, image processing, and dimensionality reduction.

      8. Applications of Matrix Algebra: Matrix algebra finds practical applications in diverse fields such as physics, computer science, data analysis, optimization, and network analysis.

      To study these topics, you can explore online resources such as textbooks, video lectures, interactive courses, and educational platforms specializing in mathematics or data science.‎

      Online Matrix Algebra courses offer a convenient and flexible way to enhance your knowledge or learn new Matrix algebra is a branch of mathematics that deals with the manipulation and study of matrices. Matrices are rectangular arrays of numbers or symbols arranged in rows and columns. In matrix algebra, various operations such as addition, subtraction, multiplication, and division are performed on matrices to solve mathematical equations and systems of equations. This branch of algebra is widely used in many fields, including computer science, physics, engineering, and economics, as it provides a powerful tool for representing and solving complex mathematical problems. skills. Choose from a wide range of Matrix Algebra courses offered by top universities and industry leaders tailored to various skill levels.‎

      When looking to enhance your workforce's skills in Matrix Algebra, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here.‎

      This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

      Other topics to explore

      Arts and Humanities
      338 courses
      Business
      1095 courses
      Computer Science
      668 courses
      Data Science
      425 courses
      Information Technology
      145 courses
      Health
      471 courses
      Math and Logic
      70 courses
      Personal Development
      137 courses
      Physical Science and Engineering
      413 courses
      Social Sciences
      401 courses
      Language Learning
      150 courses

      Coursera Footer

      Technical Skills

      • ChatGPT
      • Coding
      • Computer Science
      • Cybersecurity
      • DevOps
      • Ethical Hacking
      • Generative AI
      • Java Programming
      • Python
      • Web Development

      Analytical Skills

      • Artificial Intelligence
      • Big Data
      • Business Analysis
      • Data Analytics
      • Data Science
      • Financial Modeling
      • Machine Learning
      • Microsoft Excel
      • Microsoft Power BI
      • SQL

      Business Skills

      • Accounting
      • Digital Marketing
      • E-commerce
      • Finance
      • Google
      • Graphic Design
      • IBM
      • Marketing
      • Project Management
      • Social Media Marketing

      Career Resources

      • Essential IT Certifications
      • High-Income Skills to Learn
      • How to Get a PMP Certification
      • How to Learn Artificial Intelligence
      • Popular Cybersecurity Certifications
      • Popular Data Analytics Certifications
      • What Does a Data Analyst Do?
      • Career Development Resources
      • Career Aptitude Test
      • Share your Coursera Learning Story

      Coursera

      • About
      • What We Offer
      • Leadership
      • Careers
      • Catalog
      • Coursera Plus
      • Professional Certificates
      • MasterTrack® Certificates
      • Degrees
      • For Enterprise
      • For Government
      • For Campus
      • Become a Partner
      • Social Impact
      • Free Courses
      • ECTS Credit Recommendations

      Community

      • Learners
      • Partners
      • Beta Testers
      • Blog
      • The Coursera Podcast
      • Tech Blog
      • Teaching Center

      More

      • Press
      • Investors
      • Terms
      • Privacy
      • Help
      • Accessibility
      • Contact
      • Articles
      • Directory
      • Affiliates
      • Modern Slavery Statement
      • Manage Cookie Preferences
      Learn Anywhere
      Download on the App Store
      Get it on Google Play
      Logo of Certified B Corporation
      © 2025 Coursera Inc. All rights reserved.
      • Coursera Facebook
      • Coursera Linkedin
      • Coursera Twitter
      • Coursera YouTube
      • Coursera Instagram
      • Coursera TikTok