In numpy, we have one module linalg for all the functionalities for linear algebra. Like dot products, decomposition, solving equations etc.

We will see some of the functions with some examples. Let’s start -:

### 1. Matrix and vector products

### 1.a Dot()

### 1.b matmul()

Matmul is different in dot as follows -:

- Scalars are not allowed in matmul but in dot.

### 2. Decomposition

### -single-value-decomposition.”>2.a svd-> single value decomposition.

It will return 3 matrices It has parameters -:

- a => array whose decomosition is to perform
- full_matrices => If true then it will return U and V with dimensions(M, M) and (N, N), respectively otherwise (M, k) and (k, N), respectively. Optional(Bydefault -> True)
- compute_uv => Whether or not to compute u and v in addition to s. True by default.

For the detailed description of eigen values and eigen vectors, how are they calculated go to linear algebra portion. So, this how using either numpy or linera algebra module in numpy we can perform various linear algebra operations.

Some are present in numpy directly and some in that particular module linalg.

Hope you enjoy learning this linear algebra with numpy. Stay tuned! Keep learning.