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  • Linear Algebra

Linear Algebra Courses

Linear Algebra courses can help you learn vector spaces, matrix operations, eigenvalues, and linear transformations. You can build skills in solving systems of equations, performing dimensionality reduction, and applying concepts to data analysis and machine learning. Many courses introduce tools such as MATLAB, Python libraries like NumPy, and software for computational mathematics, that support implementing algorithms and visualizing data in various applications, including artificial intelligence and AI-driven models.


Popular Linear Algebra Courses and Certifications


  • Status: Free Trial
    Free Trial
    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
    ·
    213 reviews

    Beginner · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    I

    Imperial College London

    Mathematics for Machine Learning: Linear Algebra

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

    4.7
    Rating, 4.7 out of 5 stars
    ·
    13K reviews

    Beginner · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    D

    DeepLearning.AI

    Linear Algebra for Machine Learning and Data Science

    Skills you'll gain: Linear Algebra, NumPy, Dimensionality Reduction, Machine Learning Methods, Data Transformation, Data Manipulation, Data Science, Applied Mathematics, Mathematical Modeling, Machine Learning, Python Programming

    4.6
    Rating, 4.6 out of 5 stars
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    2.3K reviews

    Intermediate · Course · 1 - 4 Weeks

  • T

    The University of Sydney

    Introduction to Linear Algebra

    Skills you'll gain: Linear Algebra, Markov Model, Geometry, Arithmetic, Algebra, General Mathematics, Advanced Mathematics, Probability, Mathematics and Mathematical Modeling, Mathematical Theory & Analysis, Mathematical Modeling, Applied Mathematics, Statistical Methods, Engineering Analysis

    4.8
    Rating, 4.8 out of 5 stars
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    37 reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    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
    ·
    76 reviews

    Mixed · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    Linear Algebra: Linear Systems and Matrix Equations

    Skills you'll gain: Linear Algebra, Algebra, Mathematical Modeling, Engineering Analysis, Applied Mathematics, Mathematical Theory & Analysis, Geometry

    4.7
    Rating, 4.7 out of 5 stars
    ·
    160 reviews

    Beginner · Course · 1 - 4 Weeks

What brings you to Coursera today?

  • Status: New
    New
    Status: Free Trial
    Free Trial
    B

    Birla Institute of Technology & Science, Pilani

    Linear Algebra for Machine Learning & AI

    Skills you'll gain: Linear Algebra, Numerical Analysis, Artificial Intelligence and Machine Learning (AI/ML), Applied Mathematics, Data Analysis, Machine Learning, Dimensionality Reduction, Artificial Neural Networks

    Beginner · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    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, Linear Algebra, Statistical Inference, A/B Testing, Statistical Analysis, Applied Mathematics, NumPy, Probability, Calculus, Dimensionality Reduction, Numerical Analysis, Mathematical Modeling, Machine Learning, Machine Learning Methods, Data Transformation

    4.6
    Rating, 4.6 out of 5 stars
    ·
    3.1K reviews

    Intermediate · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    I

    Imperial College London

    Mathematics for Machine Learning

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

    4.6
    Rating, 4.6 out of 5 stars
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    15K reviews

    Beginner · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    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, Arithmetic, Computational Logic

    4.9
    Rating, 4.9 out of 5 stars
    ·
    4.6K reviews

    Beginner · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    Algebra: Elementary to Advanced

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

    4.8
    Rating, 4.8 out of 5 stars
    ·
    777 reviews

    Beginner · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    D
    S

    Multiple educators

    Machine Learning

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Classification And Regression Tree (CART), Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Machine Learning, Jupyter, Data Ethics, Decision Tree Learning, Tensorflow, Responsible AI, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Reinforcement Learning, Random Forest Algorithm, Feature Engineering, Python Programming

    4.9
    Rating, 4.9 out of 5 stars
    ·
    37K reviews

    Beginner · Specialization · 1 - 3 Months

Searches related to linear algebra

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linear algebra: linear systems and matrix equations
linear algebra basics
linear algebra from elementary to advanced
linear algebra: matrix algebra, determinants, & eigenvectors
linear algebra and regression fundamentals for data science
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linear algebra: orthogonality and diagonalization
1234…63

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

  • Linear Algebra from Elementary to Advanced: Johns Hopkins University
  • Mathematics for Machine Learning: Linear Algebra: Imperial College London
  • Linear Algebra for Machine Learning and Data Science: DeepLearning.AI
  • Introduction to Linear Algebra: The University of Sydney
  • Linear Algebra: Matrix Algebra, Determinants, & Eigenvectors: Johns Hopkins University
  • Linear Algebra: Linear Systems and Matrix Equations: Johns Hopkins University
  • Linear Algebra for Machine Learning & AI: Birla Institute of Technology & Science, Pilani
  • Mathematics for Machine Learning and Data Science: DeepLearning.AI
  • Mathematics for Machine Learning: Imperial College London
  • Matrix Algebra for Engineers: The Hong Kong University of Science and Technology

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 Regression (9)
Numpy (9)

Frequently Asked Questions about Linear Algebra

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:

  1. Preview the first module of many linear algebra courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  2. Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

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.‎

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.

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