We may earn an affiliate commission when you visit our partners.
David Dye,
Samuel J. Cooper,
and
A. Freddie Page
In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.
Read more
In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.
Read more
In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.
Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before.
At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning.
Register for this course and see more details by visiting:
OpenCourser.com/course/jlh27j/mathematics
Two deals to help you save
Save money when you learn. Here are two deals and offers that may be relevant to this course.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until July 14
Coursera Plus Annual
Work toward what's next for your career with access to 10,000+ programs. Discover emerging skills and save.
Up to
40%
off
Get offer
Valid until August 30
Google AI App Builder
Learn how to use Gemini API and API Studio with a three-course series from Google DeepMind
Take
100%
off
Get offer
What's inside
Syllabus
Introduction to Linear Algebra and to Mathematics for Machine Learning
In this first module we look at how linear algebra is relevant to machine learning and data science. Then we'll wind up the module with an initial introduction to vectors. Throughout, we're focussing on developing your mathematical intuition, not of crunching through algebra or doing long pen-and-paper examples. For many of these operations, there are callable functions in Python that can do the adding up - the point is to appreciate what they do and how they work so that, when things go wrong or there are special cases, you can understand why and what to do.
Read more
Syllabus
Introduction to Linear Algebra and to Mathematics for Machine Learning
In this first module we look at how linear algebra is relevant to machine learning and data science. Then we'll wind up the module with an initial introduction to vectors. Throughout, we're focussing on developing your mathematical intuition, not of crunching through algebra or doing long pen-and-paper examples. For many of these operations, there are callable functions in Python that can do the adding up - the point is to appreciate what they do and how they work so that, when things go wrong or there are special cases, you can understand why and what to do.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Teaches mathematical intuition, which helps learners understand linear algebra problems
Develops vectors and matrices, which are core skills for data-driven applications
Hands-on labs help learners apply the concepts discussed in the course
Emphasizes the practical aspects of linear algebra, which is beneficial for students interested in data science and machine learning
Provides foundational skills and knowledge in linear algebra, which is useful for further studies in machine learning
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
Save
Save
Reviews summary
Linear algebra intuition for ml
According to learners, this course provides a strong intuitive understanding of linear algebra concepts essential for machine learning. Many highlight the clear explanations and how the material is relevant to practical ML applications. The course successfully bridges the gap between theoretical math and its use in data science. While the syllabus covers key topics like vectors, matrices, eigenvalues, and eigenvectors, some feedback suggests it may be less suitable for those seeking deep mathematical rigor or prior linear algebra exposure is helpful. The practical coding examples in Python are frequently mentioned as a valuable part of the learning experience, though a few note they might be basic for experienced coders.
Some found prior knowledge beneficial or necessary.
"Some prior mathematical understanding is helpful."
"I struggled a bit without any previous linear algebra background."
"A good refresher, but might be fast-paced without prior exposure."
Accessible starting point for linear algebra.
"As a beginner, this was a great introduction."
"Very accessible if you're new to linear algebra."
"Excellent course for beginners interested in ML math."
Helpful Python examples reinforce concepts.
"The python labs were good to see the theory put into practice."
"Coding exercises really helped solidify the concepts."
"Integrating Python code is a great way to understand the practical application."
Concepts are tied directly to machine learning.
"Gave great intuition to apply linear algebra to machine learning."
"Excellent course, provides great intuition about how linear algebra applies to ML."
"Relevant examples from Machine Learning space were really helpful."
"Perfect course to understand mathematical intuition for machine learning."
Focuses on understanding concepts, not just calculations.
"It gives you the intuitive understanding of underlying mathematical concepts... rather than just knowing how to perform these operations."
"The explanations are very intuitive which helps in developing the necessary understanding."
"Great at building intuition for key concepts."
"The course does an excellent job of building intuition."
May not satisfy those seeking deep mathematical proofs.
"It does not go into the depth required for someone with an advance interest."
"Doesn't spend much time on mathematical proofs, focusing more on concepts."
"If you're looking for rigorous mathematical treatment, this might be too basic."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Mathematics for Machine Learning: Linear Algebra with these
activities:
Introduction to Linear Algebra by Gilbert Strang
Show steps
Review a classic textbook on linear algebra to strengthen your foundation. Engage with clear explanations, insightful examples, and thought-provoking exercises to enhance your understanding of core concepts.
View
Introduction to Linear Algebra (Gilbert Strang)
on Amazon
Show steps
-
Read through the assigned chapters
-
Solve the practice problems
-
Summarize key concepts
Peer Study and Problem-Solving Sessions
Show steps
Form study groups with peers to reinforce concepts, work through exercises, and prepare for assessments. Collaborative learning can enhance your understanding and build a supportive learning community.
Show steps
-
Find a study partner or group
-
Schedule regular study sessions
-
Create a study plan
-
Work through problems and exercises together
-
Discuss concepts and share insights
Solving Linear Equations and Matrix Problems
Show steps
Solve linear equation systems and practice matrix operations to enhance your algebraic skills and solidify your understanding of fundamental concepts in linear algebra.
Show steps
-
Review Gaussian elimination
-
Work through practice problems involving matrix multiplication
-
Find determinants and solve matrix equations
-
Apply these techniques to solve real-world problems
Five other activities
Expand to see all activities and additional details
Show all eight activities
Vector and Matrix Operations for Image Processing
Show steps
Develop your understanding of how vector and matrix operations are used in image processing. Implement these operations and apply them to real-world image manipulation tasks.
Show steps
-
Review vector and matrix basics
-
Learn about image representation using vectors and matrices
-
Implement image transformations, such as rotations and scaling
-
Use linear algebra techniques to analyze image data
Kaggle Linear Algebra Competition
Show steps
Challenge yourself in aKaggle competition focused on linear algebra. Apply your skills to solve real-world problems, compete with others, and enhance your practical knowledge.
Show steps
-
Identify relevant Kaggle competitions
-
Study the competition guidelines
-
Develop a solution using linear algebra techniques
-
Submit your solution and track your progress (optional)
Matrix Decomposition Techniques
Show steps
Explore different matrix decomposition techniques and their applications in machine learning algorithms. Gain hands-on experience in implementing these techniques and interpreting the results.
Browse courses on
Singular Value Decomposition
Show steps
-
Learn about the concepts of eigenvalues and eigenvectors
-
Follow tutorials on singular value decomposition and its applications
-
Implement matrix factorization techniques in a coding environment
-
Analyze and interpret the results of matrix decomposition
Data Visualization with Linear Algebra
Show steps
Utilize linear algebra concepts to visualize and explore data effectively. Create interactive visualizations that showcase your understanding of dimensionality reduction and data transformation techniques.
Show steps
-
Research different data visualization libraries
-
Learn about PCA (Principal Component Analysis)
-
Implement PCA in a coding environment
-
Create visualizations using linear algebra techniques
-
Present your findings and insights
Linear Algebra Portfolio Project
Show steps
Showcase your proficiency in linear algebra by completing a portfolio project. Select a real-world problem that can be solved using linear algebra techniques and develop a comprehensive solution.
Show steps
-
Brainstorm project ideas
-
Research and select a suitable project
-
Develop a project plan
-
Implement your solution
-
Write a project report
Introduction to Linear Algebra by Gilbert Strang
Show steps
Review a classic textbook on linear algebra to strengthen your foundation. Engage with clear explanations, insightful examples, and thought-provoking exercises to enhance your understanding of core concepts.
View
Introduction to Linear Algebra (Gilbert Strang)
on Amazon
Show steps
Show steps
Show steps
- Read through the assigned chapters
- Solve the practice problems
- Summarize key concepts
Peer Study and Problem-Solving Sessions
Show steps
Form study groups with peers to reinforce concepts, work through exercises, and prepare for assessments. Collaborative learning can enhance your understanding and build a supportive learning community.
Show steps
Show steps
Show steps
- Find a study partner or group
- Schedule regular study sessions
- Create a study plan
- Work through problems and exercises together
- Discuss concepts and share insights
Solving Linear Equations and Matrix Problems
Show steps
Solve linear equation systems and practice matrix operations to enhance your algebraic skills and solidify your understanding of fundamental concepts in linear algebra.
Show steps
Show steps
Show steps
- Review Gaussian elimination
- Work through practice problems involving matrix multiplication
- Find determinants and solve matrix equations
- Apply these techniques to solve real-world problems
Five other activities
Expand to see all activities and additional details
Show all eight activities
Vector and Matrix Operations for Image Processing
Show steps
Develop your understanding of how vector and matrix operations are used in image processing. Implement these operations and apply them to real-world image manipulation tasks.
Show steps
Show steps
Show steps
- Review vector and matrix basics
- Learn about image representation using vectors and matrices
- Implement image transformations, such as rotations and scaling
- Use linear algebra techniques to analyze image data
Kaggle Linear Algebra Competition
Show steps
Challenge yourself in aKaggle competition focused on linear algebra. Apply your skills to solve real-world problems, compete with others, and enhance your practical knowledge.
Show steps
Show steps
Show steps
- Identify relevant Kaggle competitions
- Study the competition guidelines
- Develop a solution using linear algebra techniques
- Submit your solution and track your progress (optional)
Matrix Decomposition Techniques
Show steps
Explore different matrix decomposition techniques and their applications in machine learning algorithms. Gain hands-on experience in implementing these techniques and interpreting the results.
Browse courses on
Singular Value Decomposition
Show steps
Show steps
Show steps
- Learn about the concepts of eigenvalues and eigenvectors
- Follow tutorials on singular value decomposition and its applications
- Implement matrix factorization techniques in a coding environment
- Analyze and interpret the results of matrix decomposition
Data Visualization with Linear Algebra
Show steps
Utilize linear algebra concepts to visualize and explore data effectively. Create interactive visualizations that showcase your understanding of dimensionality reduction and data transformation techniques.
Show steps
Show steps
Show steps
- Research different data visualization libraries
- Learn about PCA (Principal Component Analysis)
- Implement PCA in a coding environment
- Create visualizations using linear algebra techniques
- Present your findings and insights
Linear Algebra Portfolio Project
Show steps
Showcase your proficiency in linear algebra by completing a portfolio project. Select a real-world problem that can be solved using linear algebra techniques and develop a comprehensive solution.
Show steps
Show steps
Show steps
- Brainstorm project ideas
- Research and select a suitable project
- Develop a project plan
- Implement your solution
- Write a project report
Career center
Learners who complete Mathematics for Machine Learning: Linear Algebra will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are responsible for using data to solve complex problems and gain new insights. This course can help Data Scientists build a strong foundation in linear algebra, which is a fundamental tool for working with data. The course covers topics such as vectors, matrices, eigenvalues, and eigenvectors, which are all essential for understanding and manipulating data. Additionally, the course provides hands-on experience with Python, which is a popular programming language for data science.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and build machine learning models to solve real-world problems. This course can help Machine Learning Engineers build a strong foundation in linear algebra, which is a fundamental tool for working with machine learning models. The course covers topics such as vectors, matrices, eigenvalues, and eigenvectors, which are all essential for understanding and manipulating machine learning models. Additionally, the course provides hands-on experience with Python, which is a popular programming language for machine learning.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze data and make predictions. This course can help Quantitative Analysts build a strong foundation in linear algebra, which is a fundamental tool for working with data. The course covers topics such as vectors, matrices, eigenvalues, and eigenvectors, which are all essential for understanding and manipulating data. Additionally, the course provides hands-on experience with Python, which is a popular programming language for quantitative analysis.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to solve complex problems in business and industry. This course can help Operations Research Analysts build a strong foundation in linear algebra, which is a fundamental tool for working with data. The course covers topics such as vectors, matrices, eigenvalues, and eigenvectors, which are all essential for understanding and manipulating data. Additionally, the course provides hands-on experience with Python, which is a popular programming language for operations research.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to help businesses and organizations make informed decisions. This course can help Statisticians build a strong foundation in linear algebra, which is a fundamental tool for working with data. The course covers topics such as vectors, matrices, eigenvalues, and eigenvectors, which are all essential for understanding and manipulating data. Additionally, the course provides hands-on experience with Python, which is a popular programming language for statistics.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and make financial decisions. This course can help Actuaries build a strong foundation in linear algebra, which is a fundamental tool for working with data. The course covers topics such as vectors, matrices, eigenvalues, and eigenvectors, which are all essential for understanding and manipulating data. Additionally, the course provides hands-on experience with Python, which is a popular programming language for actuarial science.
Financial Analyst
Financial Analyst
Financial Analysts use mathematical and statistical techniques to analyze financial data and make investment recommendations. This course can help Financial Analysts build a strong foundation in linear algebra, which is a fundamental tool for working with data. The course covers topics such as vectors, matrices, eigenvalues, and eigenvectors, which are all essential for understanding and manipulating data. Additionally, the course provides hands-on experience with Python, which is a popular programming language for financial analysis.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand consumer behavior and trends. This course can help Market Researchers build a strong foundation in linear algebra, which is a fundamental tool for working with data. The course covers topics such as vectors, matrices, eigenvalues, and eigenvectors, which are all essential for understanding and manipulating data. Additionally, the course provides hands-on experience with Python, which is a popular programming language for market research.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make informed decisions. This course can help Business Analysts build a strong foundation in linear algebra, which is a fundamental tool for working with data. The course covers topics such as vectors, matrices, eigenvalues, and eigenvectors, which are all essential for understanding and manipulating data. Additionally, the course provides hands-on experience with Python, which is a popular programming language for business analysis.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make informed decisions. This course can help Data Analysts build a strong foundation in linear algebra, which is a fundamental tool for working with data. The course covers topics such as vectors, matrices, eigenvalues, and eigenvectors, which are all essential for understanding and manipulating data. Additionally, the course provides hands-on experience with Python, which is a popular programming language for data analysis.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course can help Software Engineers build a strong foundation in linear algebra, which is a fundamental tool for working with data. The course covers topics such as vectors, matrices, eigenvalues, and eigenvectors, which are all essential for understanding and manipulating data. Additionally, the course provides hands-on experience with Python, which is a popular programming language for software development.
Computer Scientist
Computer Scientist
Computer Scientists design and develop computer systems and software applications. This course can help Computer Scientists build a strong foundation in linear algebra, which is a fundamental tool for working with data. The course covers topics such as vectors, matrices, eigenvalues, and eigenvectors, which are all essential for understanding and manipulating data. Additionally, the course provides hands-on experience with Python, which is a popular programming language for computer science.
Mathematician
Mathematician
Mathematicians study the patterns and structures of numbers, shapes, and other mathematical objects. This course can help Mathematicians build a strong foundation in linear algebra, which is a fundamental tool for working with data. The course covers topics such as vectors, matrices, eigenvalues, and eigenvectors, which are all essential for understanding and manipulating data. Additionally, the course provides hands-on experience with Python, which is a popular programming language for mathematics.
Physicist
Physicist
Physicists study the laws of nature and the behavior of matter and energy. This course can help Physicists build a strong foundation in linear algebra, which is a fundamental tool for working with data. The course covers topics such as vectors, matrices, eigenvalues, and eigenvectors, which are all essential for understanding and manipulating data. Additionally, the course provides hands-on experience with Python, which is a popular programming language for physics.
Engineer
Engineer
Engineers design, develop, and maintain machines, structures, and other systems. This course can help Engineers build a strong foundation in linear algebra, which is a fundamental tool for working with data. The course covers topics such as vectors, matrices, eigenvalues, and eigenvectors, which are all essential for understanding and manipulating data. Additionally, the course provides hands-on experience with Python, which is a popular programming language for engineering.
For more career information including salaries, visit:
OpenCourser.com/course/jlh27j/mathematics
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Mathematics for Machine Learning: Linear Algebra.
Classic textbook on linear algebra that provides a comprehensive overview of the subject. It is well-written and accessible to students with a variety of backgrounds.
Comprehensive and up-to-date treatment of statistical learning. It covers a wide range of topics, including linear regression, logistic regression, and decision trees.
Classic textbook on statistical learning. It provides a comprehensive and in-depth look at the subject.
Comprehensive and up-to-date treatment of multivariate statistical techniques. It covers a wide range of topics, including linear regression, logistic regression, and discriminant analysis.
Classic textbook on multivariate statistical analysis. It provides a comprehensive and in-depth look at the subject.
Save
Classic reference on matrix computations. It provides a comprehensive overview of the subject and valuable resource for students and researchers alike.
Comprehensive and up-to-date treatment of numerical linear algebra. It valuable resource for students and researchers who need to use numerical methods to solve linear algebra problems.
Comprehensive and up-to-date treatment of the mathematics that is essential for machine learning. It covers a wide range of topics, including linear algebra, calculus, and probability.
Save
Comprehensive and up-to-date treatment of deep learning. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks.
More advanced treatment of linear algebra that is suitable for students with a strong mathematical background. It provides a rigorous and in-depth look at the subject.
Modern and accessible introduction to statistical methods for data science. It covers a wide range of topics, including linear regression, logistic regression, and decision trees.
Practical guide to machine learning using Python. It covers a wide range of topics, including data preprocessing, model training, and model evaluation.
Modern and accessible introduction to linear algebra that is suitable for students with a variety of backgrounds. It provides a clear and concise overview of the subject.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/jlh27j/mathematics
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Rating
4.7
Effort
Approx. 18 hours
Level
Introductory
Via
Coursera
Institution
Imperial College London
Instructors
David Dye
Samuel J. Cooper
A. Freddie Page
Language
English
Transcripts
العربية
Français
বাংলা
українська мова
中文
Ελληνικά
Italiano
Português
tiếng Việt
Nederlands
한국어
Deutsch
پښتو
اُردُو,
Pусский
ไทย
Bahasa Indonesia
Svenska
Türkçe
آذربایجان دیلی
Español
हिंदी
日本語
қазақша
فارسی
magyar nyelv
Polski
This course belongs to a
collection
called
Mathematics for Machine Learning. It's comprised of three courses:
- Mathematics for Machine Learning: PCA
- Mathematics for Machine Learning: Linear Algebra (viewing)
- Mathematics for Machine Learning: Multivariate Calculus
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2025 OpenCourser