In Machine Learning we use few mathematical terms that are important and frequently used, with this article I tried to list down those important keywords. When we walk on ML, we need a clear visualization of these terms so that we can understand what we want to achieve by using the algorithms. Machine learning is a like a toy if we read it without the deep understanding of Derivative, vectors, and probability. Derivative help in understanding the minimization of the cost function, Vectors help in understanding the overall movement and probability helps in understanding the occurrence of a conditional event.

#### Vectors

There are few quantities in the universe which we cannot define only with magnitude. We need direction as well like if we say we are applying force we need to know the direction as well. There are many terms related to vector we discuss one by one.

**Codirected vectors**

Two collinear vectors a and b are called codirected vector if their directions are the same: a↑↑b

**Colinear Vector:**

Vector parallel to one line or lying on one line are called Colinear vector.

**Zero vector**

It is a vector whose start and end points coincide.

**Matrix**

In mathematics, a matrix is a rectangular array of numbers arranged in rows and columns. In python, Numpy holds the power to sketch n-dimensional array.The matrix can hold attributes/features that are placed in columns. So a single row represents an event with a set of features.

import numpy as np mat=np.arange(36).reshape(6,6)