# Hands-on: Linear algebra

Explore NumPy's in-built linear algebra routines
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In this exercise you can will explore NumPy’s in-built linear algebra routines

Source code for this exercise is located in numpy/linear-algebra/

1. Construct two symmetric 2×2 matrices A and B.
Hint: a symmetric matrix can be constructed easily from a square matrix
as Asym = A + A^T
2. Calculate the matrix product C = A * B using numpy.dot().
3. Calculate the eigenvalues of matrix C with numpy.linalg.eigvals().
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