5 Numpy routines and their applications

Numpy is the fundamental package for scientific computing with Python

Nearly every scientist working in Python draws on the power of NumPy.

NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use.

With this power comes simplicity: a solution in NumPy is often clear and elegant.

In this notebook I've analyzed 5 numpy functions which I felt are useful apart from the trivial numpy functions,

They are as follow:

- numpy.poly1d

- numpy.polyder

- numpy.genfromtxt

- numpy.fromregex

- numpy.savetxt

!pip install jovian --upgrade -q

import jovian

Let's begin by importing Numpy and listing out the functions covered in this notebook.
Later in the notebook I've used numpy polynomial methods to implement the Gradient Descent algorithm which is extensively used in regression problems to minimize loss.

import numpy as np