What skills can I learn in linear algebra courses on Coursera?

Linear algebra courses on Coursera cover a wide array of fundamental and advanced topics, enabling learners to: Understand vectors and matrices and their applications in various fields Learn methods for solving systems of linear equations Explore eigenvalues and eigenvectors and their significance in transformations Apply linear algebra concepts to computer graphics , data analysis , and more Discover how linear algebra supports machine learning and artificial intelligence algorithms

Do I need prior experience in mathematics to start these linear algebra courses?

No prior experience in advanced mathematics is required to start with the introductory linear algebra courses available on Coursera. These courses are designed to accommodate learners at different proficiency levels: Beginners can start with basic concepts of vectors and matrix operations. Intermediate learners can delve into more complex subjects such as linear transformations and space. Advanced learners can tackle topics like abstract vector spaces and computational linear algebra.

What certifications and credentials can I earn through linear algebra courses on Coursera?

Coursera offers various credentials through its linear algebra courses, designed to cater to both novices and professionals: Course certificates that validate your knowledge of linear algebra fundamentals Professional certificates that demonstrate your specialized skills in mathematics Specializations with focused expertise in advanced linear algebra topics

How can I purchase linear algebra training courses for my team?

Looking to enhance your team's linear algebra skills? Coursera provides tailored enterprise solutions for teams ranging of 5-125 employees. Our offerings include advanced analytics, customized learning paths, and collaborative tools. To explore our linear algebra training options and make a purchase, please visit our Coursera for Teams page.

Top Linear Algebra Courses - Learn Linear Algebra Online

Browse
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
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
208 reviews
Beginner · Specialization · 3 - 6 Months
Status: Free Trial
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
12K reviews
Beginner · Course · 1 - 3 Months
Status: Free Trial
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
2.2K reviews
Intermediate · Course · 1 - 4 Weeks
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
35 reviews
Intermediate · Course · 1 - 4 Weeks
Status: Free Trial
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
156 reviews
Beginner · Course · 1 - 4 Weeks
Status: Free Trial
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
15K reviews
Beginner · Specialization · 3 - 6 Months

What brings you to Coursera today?

Start my career

Change my career

Grow in my current role

Explore topics outside of work

Status: Free Trial
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
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
75 reviews
Mixed · Course · 1 - 3 Months
Status: Free Trial
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
3K reviews
Intermediate · Specialization · 1 - 3 Months
Status: New
Status: Free Trial
Birla Institute of Technology & Science, Pilani
Linear Algebra for Machine Learning & AI
Skills you'll gain: Linear Algebra, Artificial Intelligence and Machine Learning (AI/ML), Applied Mathematics, Numerical Analysis, Machine Learning, Artificial Neural Networks, Dimensionality Reduction, Data Analysis
Beginner · Course · 1 - 3 Months
Status: Free Trial
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
771 reviews
Beginner · Specialization · 3 - 6 Months
Status: Free Trial
University of Colorado Boulder
Essential Linear Algebra for Data Science
Skills you'll gain: Linear Algebra, Applied Mathematics, Data Science, Data Analysis, Statistical Methods, Algebra, Numerical Analysis
4.5
Rating, 4.5 out of 5 stars
183 reviews
Intermediate · Course · 1 - 3 Months

Searches related to linear algebra

linear algebra from elementary to advanced

linear algebra for machine learning and data science

linear algebra: linear systems and matrix equations

linear algebra: matrix algebra, determinants, & eigenvectors

linear algebra for machine learning & ai

linear algebra basics

linear algebra and regression fundamentals for data science

linear algebra: orthogonality and diagonalization

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

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 Linear Algebra

Linear algebra courses on Coursera cover a wide array of fundamental and advanced topics, enabling learners to:

Understand vectors and matrices and their applications in various fields
Learn methods for solving systems of linear equations
Explore eigenvalues and eigenvectors and their significance in transformations
Apply linear algebra concepts to computer graphics, data analysis, and more
Discover how linear algebra supports machine learning and artificial intelligence algorithms‎

No prior experience in advanced mathematics is required to start with the introductory linear algebra courses available on Coursera. These courses are designed to accommodate learners at different proficiency levels:

Beginners can start with basic concepts of vectors and matrix operations.
Intermediate learners can delve into more complex subjects such as linear transformations and space.
Advanced learners can tackle topics like abstract vector spaces and computational linear algebra.‎

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.