How Python Compliments Machine Learning

The following article takes a look at how Python is compatible with writing Machine Learning programs.

Python is a fairly old language that was introduced in 1989 by Guido Van Rossum and made public in 1991. The recent rise in the usage of Python can be related to one simple thing - The introduction and implementation of Machine Learning in the corporate fields for the pursuit of Artificial Intelligence.
 
However, the question still remains as to why Python was chosen?
 

How is Python different from the others?

 
Python is a high-level interpreted language that is programmer-friendly, meaning that the Python codes are easy to understand and do not require any effort from the program as is required by several other languages. Some of the examples are given below.
 
In C language, the code to print out ‘Hello World’ is as below.
 
How Python Compliments Machine Learning
 
The functions need to be called and specified first in C.
  1. #include <stdio.h>  
  2. int main()  
  3. {  
  4.  printf("Hello World");  
  5. return 0;   
  6. }  
The same program to say ‘Hello World’ in Java is coded as below.
 
How Python Compliments Machine Learning
  1. public class HelloWorld {  
  2.    public static void main(String[ ] args){  
  3.       System.out.println("Hello, World");  
  4.    }  
  5. }   
The code to print ‘Hello World’ in Python is just one line, as shown ahead.
 
How Python Compliments Machine Learning
  1. print("Hello World")  
When compared to C and Java, the difference is huge. The amount of lines of code required in the case of C and Java is far more than that of Python which executes the similar code in just a single line.
 

What else does it offer?

 
Python offers a better ability for data handling despite being slower than other languages which can be defined and then used in other programs.
 
How Python Compliments Machine Learning

 
Can Python files be used in other languages?

 
Python is also compatible with other languages as files written in other languages can be easily transformed into Python and vice-versa, allowing for far more applications.
 
For example, in C, a Python file can be included as below.
  1. #include “Python.h”  
In Java, a Python file can be run like this.
  1. String cmd = “python/”;  
  2. String py = “file”;  
  3. String run = “python” + cmd + “.py”;  
  4. System.out.println(run);  
  5. //Runtime.getRuntime().exec(run);  
  6. Process p = Runtime.getRuntime().exec(“python file.py);  
And similar methods exist for other languages too.
 

Is there something special about Python that others don’t have?

 
Python is, therefore, much more preferred on the fact of it being able to provide portability and ease. A simple Python command when showed to a grade school student can be easily understood if they are able to understand English. This puts Python at a huge advantage when heaps of code is to be handled within a limited amount of time, which is a common situation in the field of Machine Learning.
 
The icing on the top of the cake in case of Python is its readily available predefined directories and libraries that reduce the time needed to code immensely, such as -  NumPy, pandas, SciPy, matplotlib, PyGame, etc.
 
With each library coming with preloaded tools to make the code easier to program towards the main objective, Python is a language that offers so much potential that it makes the vision of several programs that were before thought to be hard and arduous very easy.
 
Apart from the libraries and directories that can be installed as per the wish of the user, allowing the user to customize their own Python library is a huge advantage. Python can be accessed from many platforms, such as -  Jupyter, Spyder, Microsoft Azure (a platform specifically for the designing and creation of Machine Learning Programs), Eclipse, etc.
 
This makes Python one of the best languages for Machine Learning.