Start where you are — each level maps to a different point on the journey. We confirm the right one together in your free first session.
01$55/hr
Linear Algebra I
Vectors, matrices, systems.
Who it's forFor the first-year student or self-learner starting linear algebra — vectors, matrices, and solving systems of equations from the ground up, with no prior exposure assumed.
Understand linear algebra from the ground up: vectors, matrices, and solving systems of equations — with the geometric intuition that makes it…
What you'll be able to do
Add, scale, and multiply matrices and vectors correctly, and reason about their dimensions.
Solve a linear system with Gaussian elimination (row reduction to RREF) reliably.
Compute a determinant and use it to tell whether a matrix is invertible.
Interpret a system's solution set as a unique solution, no solution, or infinitely many.
Sounds familiar?
Matrix multiplication rules feel arbitrary and I keep getting the dimensions wrong.
Row reduction works when I watch it, but on my own I make a sign error and the whole thing collapses.
I can compute things but I have no idea what a determinant actually means.
I don't understand when a system has one solution, none, or infinitely many.
Vectors & matricesMatrix operationsSystems of equationsDeterminants
Who it's forFor the student moving from mechanical matrix computation into the conceptual core of linear algebra — vector spaces, linear transformations, and eigenvalues.
Master the core that university courses test and ML relies on: vector spaces, linear transformations, and eigenvalues and eigenvectors — explained…
What you'll be able to do
Decide whether a set is a subspace and find a basis and the dimension of a space.
Represent a linear transformation as a matrix and connect it to its null space and column space.
Compute eigenvalues and eigenvectors, diagonalise a matrix, and explain them geometrically.
Apply Gram-Schmidt to build an orthonormal basis and project a vector onto a subspace.
Sounds familiar?
Abstract vector spaces, span, and basis are where the course suddenly stopped making sense.
I can find eigenvalues by rote but I genuinely don't know what an eigenvector represents.
Proof-style questions about subspaces and independence intimidate me.
Orthogonality and Gram-Schmidt feel like a procedure I follow blindly.
Who it's forFor the data-science or ML learner who knows the matrix mechanics and now wants the decompositions that power real algorithms — SVD, PCA, and least squares — and to see where they live in ML.
See why linear algebra is the language of machine learning: SVD, PCA, least squares and how matrix decompositions power real models — taught by an…
What you'll be able to do
Compute and interpret an SVD, and explain the role of singular values and the three factors.
Derive PCA from the covariance matrix (or via SVD) and use it to reduce dimensionality on real data.
Solve a least-squares problem with the normal equations and recognise it inside linear regression.
Explain where these decompositions appear in ML (embeddings, dimensionality reduction, regression).
Sounds familiar?
I use PCA from scikit-learn but I have no idea what it's doing to my data underneath.
SVD gets name-dropped in every ML course and I never learned what the three matrices mean.
I know least squares is the line of best fit but not the linear-algebra reason it works.
I can't connect the eigenvalue theory I learned to anything I actually do in ML.
SVD & decompositionsPCALeast squaresLinear algebra in MLNumerical methods
ToolsNumPy (numpy.linalg)scikit-learn (PCA, LinearRegression)Python / JupyterPen & paper
No guarantees, no fixed curriculum — just a specific, repeatable way of working that gets you unstuck on Linear Algebra.
01
Built around your goal
There is no fixed syllabus to keep pace with. The hour is built backwards from the one thing you need — a failing assignment, a concept that will not stick, a project to ship.
02
Diagnosed, not re-taught
We find the precise step where it breaks down instead of re-covering what you already know — so the time goes to the gap that actually matters.
03
You drive, I steer
You do the work in real time while I guide — that is how it sticks. You leave able to do it yourself, not just having watched me do it.
04
Honest pace & pricing
You only pay for the levels and pace that fit. We agree the plan together after the free first session — no packages you do not need.
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Frequently asked questions
About Linear Algebra tutoring and how sessions work.
Is the first Linear Algebra session really free?
Yes. Your first session is complimentary so you can experience the teaching style, talk through your goals, and decide whether to continue — no credit card required upfront.
How much does Linear Algebra tutoring cost?
Sessions start at $55/hour, and multi-session packages are available at a discount. You only pay for the levels and pace that fit your goals — we agree on a plan together after the free first session.
How are Linear Algebra sessions delivered?
All sessions are 1-on-1 and 100% online over video, with screen sharing and a shared editor or whiteboard. Sessions are typically 60–90 minutes and scheduled around your availability.
Which Linear Algebra level should I start at?
It is set by where you are now, not a fixed curriculum. In the free first session we map your background to the right starting level and adjust the pace as you progress.
Who is teaching the sessions?
Every session is taught directly by Ali Jabbary, M.Sc., P.Eng. — not a rotating pool of tutors. You work with the same instructor throughout.
