<jupyter_start><jupyter_text>Using Python Codes, to find the inverse of a matrix, the matrix is passed to linalg.inv() of the Numpy Module:<jupyter_code>import numpy as np 

a = np.array([[4, 3], [-5,9]])
print(a)
inv_a = np.linalg.inv(a)
print(inv_a)
b = np.array([[20],[26]])
print(b)
x = np.linalg.inv(a).dot(b)
print(x)

x = np.dot(inv_a,b)
print(x)<jupyter_output>[[2.]
 [4.]]<jupyter_text>Application<jupyter_code>a = np.array([[20, 10], [17, 22]])
print(a)

inv_a1 = np.linalg.inv(a)
print(inv_a)
b = np.array([[350], [500]])
print(b)
x1 = np.dot(inv_a1, b)
print(x1)<jupyter_output>[[10.]
 [15.]]<jupyter_text>ans if correct<jupyter_code>c = np.dot(a, x1)
print(c)<jupyter_output>[[350.]
 [500.]]<jupyter_text>activity<jupyter_code>c = np.array([[4, 3, 2], [-2, 2, 3], [3, -5, 2]])
print(c)

d = np.array([[25], [-10], [-4]])

x = np.linalg.inv(c).dot(d)
print(x)<jupyter_output>[[ 4  3  2]
 [-2  2  3]
 [ 3 -5  2]]
[[ 5.]
 [ 3.]
 [-2.]]
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