Vectors you can work with
Represent vectors, add, subtract and scale them, and picture how they describe data in practical scenarios.






























A seven-hour introduction to linear algebra for AI and data science. It opens with a refresher on the math you need, then covers vectors, matrices and the dot product.
From there you solve linear systems with Gaussian elimination and Reduced Row Echelon Form, and finish with a preview of symmetric and diagonal matrices, how linear algebra is coded and where it’s applied.
Represent vectors, add, subtract and scale them, and picture how they describe data in practical scenarios.
Tell the main types of matrix apart, add, subtract and multiply them, and see how matrices carry out transformations of data.
Read the dot product as a geometric idea tied to vector length, and use it with the cosine rule to solve problems.
Solve linear systems with Gaussian elimination, and use Reduced Row Echelon Form to reach the solutions cleanly.
Refresh the essential math prerequisites and get a high-level view of how linear algebra is applied across AI, data science and emerging technologies.
Build a working understanding of vectors: how they are represented, how to add, subtract and scale them, and how they appear in practical data scenarios.
Learn the different types of matrix, how they structure data and how to add, subtract and multiply them, then see how matrices power data transformations.
Understand the dot product, its geometric meaning and its link to vector length, then apply the cosine rule and related ideas to solve problems.
Learn the idea of solving a system of linear equations with Gaussian elimination, and how Reduced Row Echelon Form streamlines finding the solutions.
Get a first look at specialized matrices such as symmetric and diagonal ones, how linear algebra is written in code, and its broad applications.

Instructor · LUNARTECH
Tatev brings industry experience, advanced AI/ML degrees and a reputation as a thought leader in data science to LUNARTECH. She holds Bachelor’s and Master’s degrees in STEM, and her publications in top journals and presentations at international conferences showcase her expertise.
Review the math prerequisites, then meet vectors, matrices and the operations that combine them.
Use the dot product for length and angle, and apply the cosine rule to solve problems.
Reduce a linear system with Gaussian elimination, then read the solutions off its reduced row echelon form.
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Every role uses all six modules. Here’s where each one leans hardest, and where its work ends up.
Treats a dataset as vectors and matrices. Reads similarity between records off a dot product.
Vectors, Matrices, Dot product
Data science teams
Follows what the matrices inside a model are doing. Solves the small systems that sit underneath it.
Matrices, Linear systems, Preview
AI and machine learning
Organizes records into matrices before analysis. Uses vector operations to compare and combine them.
Vectors, Matrices
Data science teams
Reads the linear algebra inside a published method. Writes the idea as code once the notation is clear.
Crash course, Dot product, Preview
AI and machine learning, Research
Treats a dataset as vectors and matrices. Reads similarity between records off a dot product.
Over 10,000 students trust LUNARTECH
Understand systems all the way down. Build the ones others rely on.
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Updated November 2026.
Need help? These answers cover most of what people ask before they start.
As a preview. The last module shows how linear algebra is coded and where it’s applied, alongside specialized matrices such as symmetric and diagonal ones.
With a quick refresher on the essential math prerequisites and an overview of how linear algebra is applied across AI and data science.
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Seven hours from a refresher through vectors, matrices and linear systems to a preview of what comes next. Begin with a free lesson, or enroll in the full course.