NumPy linear algebra
```python import numpy as np a = np.array([1, 2]) b = np.array([3, 4]) dot_product = np.dot(a, b) print(dot_product) ``` Output: ``` 11 ```
In linear algebra NumPy,we use the solve () function to solve a system of linear equations.For a given matrix A and a vector.
Linear algebra deals with mathematical concepts related to linear equations and their representations using matrices.NumPy provides us with functions for performing common linear algebra tasks,such as array multiplication, solving linear systems,and more linear algebra is the study of linear combination.It is the study of vector spaces, lines and planes,and some mappings that are required to perform the linear transformation.It includes vectors,matrices and linear functions.It is the study of linear sets of equation and its transformation properties.A linear algebra in two variables is of the form Ax+by+c=,in which A and B are the coefficient,C is a constant term,and x and y are to the two variables,each with a degree of 1 For example,7x+9y+4=0 is a linear equations in two variables.It is a tool used to analyse and solve problems related to almost every aspect of our physical work and our society.In particularly, linear algebra is one of the most useful devices on the mathemations in almost every displine,ranging from electronics to psychology.NumPy’s main object is the homogeneous multidimensional array.It is a table of elements all o the same type, indexed by a tuple of non-negative integers.In NumPy dimensions are called axes.For example, the array for the coordinates of a point in3D space,[1,2,3] has one axis.NumPy used is a python library usede for working with arrays