Why Python Is An Excellent Choice For Machine Learning
I was always curious as to why Python stands on the top of Machine Learning even when so many other languages exist.
I wasn’t until years later that I found out why this was. In this article, I will first compare why Python takes the lead in the race and leaves C++ and Java in the dust. I will then introduce you to the libraries which act as the gazillion horsepower engine for Python. In the end, I will go through a point on which I disagree with others on why Python came to be where it is now in machine learning.
There is too much for me to write and too much for you to read so let’s not waste any more time and let’s get started.
Python Vs C++
C++ is fast. Crazy fast. So fast that even Python cannot outperform C++. C++ gains this advantage by being a statistically typed language. It is able to create a compact and faster running code.
But, Python had an advantage up its sleeves.
When we work with Artificial Intelligence, Machine Learning, or Deep Learning:
- Things tend to get complicated very soon.
- The code changes hands often.
- Platforms on which the code runs, change.
For these three reasons alone, working with C++ becomes a nightmare while working with Python solves all these problems:
- Python has fairly simple syntax.
- No matter how many hands the code changes it always remains fairly understandable without much effort.
- It runs on all platforms without much hindrance.
I don’t know much about others but believe me I have outsourced a number of projects just because setting up the environment on a specific platform in order to work with them was a nightmare.
Python Vs Java
There is not much to say in this section… other than a few basic things.
Java is slower than Python. I can end the “Python Vs Java” section here but then it would look like I am just being lazy so, here is another thing, Java is a compiled language while Python is an interpreted language. Just to be clear on this, compiled indicates that the program can only run on the platform it was compiled on and thus Java bites the dust.
Python’s Arsenal Of Libraries
One of the main reasons why Python so quickly became a staple of machine learning was its extensive libraries.
You want a complex computational operation done on a massive amount of data? Python has the library for it.
Want to work with images?
Want to solve complex machine learning problems?
Want to make sense of a massive amount of messy data?
Interested in deep learning, which is basically a specialized version of machine learning?
Other than a library to actually take you to the moon Python has a library for any and every function that might be required of it.
With this arsenal of libraries, Python took over machine learning like Netflix took over Blockbuster.
The most notable libraries Python has to offer for Machine learning are some of the following. This might get a little technical for someone like me from 5 years ago so I suggest you put your technical hat on.
This is a modular machine learning library for Python and provides simple yet effective algorithms for Machine learning tasks. It also provides environments to test and compare different algorithms and learn how they work.
This a bilateral framework that focuses on support vector machine(SVM) and kernel methods.
This an open source general purpose machine learning library. The major use of this library is in data analysis.
I can go on with just the libraries for another 10 pages but I think this should suffice for now.
Now, let’s move on to the point that I don’t agree with. When someone says that these are the reasons why Python is at the top of machine learning, I say “This Means War”
My Personal Disagreement
I did a lot of research when I decided to write an article on this topic and I was furious. I just thought it was a few people around me that think like this, but no, I was wrong. Each and every article written by an average joe and tech billionaire alike, there was this one point common. I rephrase it so I can present it in a few lines.
“Python is excellent for machine learning because it is easy to learn for beginners”
Now I can go on and write an essay on how many levels this statement seems wrong but I will try to keep my words concise, simple and to the point.
No matter how difficult the language would have been. Python would still be where it is now in machine learning. The difficulty has never stopped us developers from taking a step back.
A beginner means someone who doesn’t know how to program. Now I am not saying that it is not easy, it is easy but this does not result in developers taking up machine learning over other potential career paths. Easiness has nothing to do with the popularity of Python. It is purely because of the benefits and features.
You can understand what my point of view is with just these two points. I can go on and give a dozen more but I have to go back to working on my project. I wrote this article in a manner that if I from the past was reading it, it would make sense to him. And I believe many of the readers who are reading this article will feel the same way.
By the end of the day, it’s the library of Python that makes it the champion of machine learning. It’s the ability to work on all platforms that make Python popular and widely used.