Transformations you can picture
Explain what a linear transformation preserves and show how it rotates, scales, reflects or distorts shapes and space.






























A five-hour course that starts from the geometry of linear transformations, how they rotate, scale, reflect and distort shapes, and works toward the matrices that represent them.
Along the way you cover determinants, inverses, the transpose, vector spaces, bases and projections. It ends on matrix factorization: special matrices, then QR, eigen and singular value decomposition.
Explain what a linear transformation preserves and show how it rotates, scales, reflects or distorts shapes and space.
Add, subtract and scale matrices, and use determinants, inverses and the transpose to describe and undo transformations.
Work with a vector space and its basis, and use vector projections to solve geometric problems.
Recognize special matrices and follow the ideas behind QR, eigen and singular value decompositions.
Start with linear transformations, the mathematical operations that change space while preserving specific properties.
Learn to visualize a transformation and see how it rotates, scales, reflects and distorts shapes.
Work through matrix addition, subtraction and scalar multiplication, the basic tools for representing linear transformations.
Use determinants to learn key facts about a matrix, inverses to undo a transformation, and the transpose to manipulate matrices.
Understand what a vector space is and what its basis is, and use vector projections as a geometric tool.
Meet special matrices with unique properties, then break matrices into simpler components with QR, eigen and singular value decomposition.

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.
Start with what a linear transformation does to shapes before you work with any matrix.
Represent the transformation with a matrix, then use its determinant and inverse to read and reverse it.
Finish by factorizing a matrix into simpler components with QR, eigen and singular value decomposition.
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Every role uses all six modules. Here’s where each one leans hardest, and where its work ends up.
Applies transformations to images and shapes. Undoes a transformation to get back to the original.
Transformations, Geometry, Det, inverse, T
Image processing
Reduces a large matrix to its strongest parts. Knows when a factorization is the right tool.
Vector spaces, Factorization
Data compression, Data science
Reads a model’s matrices as transformations of space. Uses decompositions to simplify or compress them.
Matrices, Det, inverse, T, Factorization
Recommendation systems, Data science
Rotates, scales and reflects objects with matrices. Checks what a transformation does to shape and area.
Transformations, Geometry, Matrices
Image processing
Applies transformations to images and shapes. Undoes a transformation to get back to the original.
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Updated November 2026.
Need help? These answers cover most of what people ask before they start.
Special matrices and matrix factorization: breaking a matrix into simpler components with QR, eigen and singular value decomposition.
Yes. One module covers vector spaces, their basis and vector projections.
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Five hours from linear transformations to QR, eigen and singular value decompositions. Begin with a free lesson, or enroll in the full course.