Math, Matrix, Linear Algebra, Linear Regression Python, Linear Regression.
np.array([[0.,-3.,-2.],[1.,-4.,-2],[-3.,4.,1.]]) print(f'm1 is {m1}') m1_inv = np.linalg.inv(m1) print(f'm1 inverse is {m1_inv}') with the answer given as: Easy! In fact, if we take random values for the matrix M, it would work almost every time (e.g. you wouldn’t live long enough to witness one