The **numpy.linalg.inv()** function computes the inverse of a matrix. The function takes a square matrix as input and returns a square matrix as output. The output matrix is the inverse of the input matrix.

The inverse of a matrix is that matrix which, when multiplied with the original matrix, results in an identity matrix.

**Equation For Getting Inverse Of A Matrix**

```
A*x= B
A^-1 A*x= A-1 B
x= A-1 B
```

**Where** A^-1: It denotes the inverse of a matrix.

x: It denotes an unknown column.

B: It denotes the solution matrix.

Now, let’s see the procedure for using Numpy to find the inverse of a matrix.

**Syntax**

`numpy.linalg.inv(A) `

**Parameters**

A: It denotes the Matrix to be inverted.

**Return Value**

The inverse of Matrix A is returned.

**Example 1**

```
import numpy as np
A = np.array([[-5, -2, 3, 4],
[3, 1, 2, 7],
[2, 7, -5, 2],
[6, -6, 8, 4]])
print(np.linalg.inv(A))
```

**Output**

```
[[-0.19186047 0.1627907 -0.11046512 -0.0377907 ]
[ 0.64534884 -1.09302326 1.09883721 0.71802326]
[ 0.73837209 -1.23255814 1.12209302 0.85755814]
[-0.22093023 0.58139535 -0.43023256 -0.33139535]]
```

**Explanation**

Here, A matrix was given as input to the function, and then the Inverse of a matrix was returned as the output.

**Example 2**

```
import numpy as np
A = np.array([[[3., 4.], [4., 5.]],
[[6, 7], [7, 9]]])
print(np.linalg.inv(A))
```

**Output**

```
[[[-5. 4. ]
[ 4. -3. ]]
[[ 1.8 -1.4]
[-1.4 1.2]]]
```

**Explanation**

Here, we gave several matrices as an input to the function, and then the inverse of a matrix was returned as the output.

That is it.