Python was always doing well because of its stable and easily maintainable nature, like a popular teen going through the high school corridors, python has been catching a lot of eyeballs in recent years.TIOBE Index named it the language of the year, four times so far since its inception.
Why is it so good for Machine Learning and why aren’t other languages like C, C++, java, not the best match you ask?
This is because Python is equipped with a host of libraries and frameworks for developers to use. In a field where complex algorithms are used so frequently one doesn’t have to start the whole process from scratch with Python saving both time and resources.
How Is That Important You Ask?
Imagine a fine morning and you decide to get ready and leave to work early, but then you had to invent the wheel all over again, build yourself a vehicle and then get to work, this would leave you so tired and frustrated that you would manage to get no work done at all and return home gloomy (unless you are coffee person XD)
Python aims to avoid just that, take the library Sklearn for example! it gives you a host of Supervised and Unsupervised algorithms that are ready to use and can be directly imported to use in your code.
Why Do Those Developers Love Python in Their Machine Learning And AI Projects?
Python is Flexible:
- Python is best suited for Machine Learning projects because it allows great flexibility in its structure, you could choose to use OOPs or go the normal scripting way, it doesn’t really matter to Python.
- Machine Learning projects would need a lot of recompiling, especially those involving Neural Networks, Python supporting platforms such as Jupyter and Google Colab allows you to recompile just a part of the code rather than the whole code saving you valuable time, one can only truly appreciate this feature if one had to recompile the entire code just because of a simple error.
- What’s better is that Python is actually very friendly with other languages, as a result, you can combine Python with other languages helping developers get the desired output much soon.
Python Is Platform-Independent :
Python is platform-independent, running on platforms such as Windows, Linux, and a whole host of other platforms. A developer can always use packages like Pyinstaller to make their code run on other platforms.
Python Has Excellent Readability:
If you have ever tried reading another person’s code you would emphasize the frustration and anger that comes along with it. Thankfully, Python doesn’t add to this because of its painlessly simple code, making sure that your peers can understand, share and copy your code and use it in their own solution. This leads to better algorithms, research, and tools being developed in the long run.
Python Is Easy To Learn:
Python unlike other languages doesn’t have too many complex syntax and restrictions, allowing you to write more freely. This is perhaps the reason why so many people are switching over to Python since it is so easy to pick up and master. One always appreciates Python if one has tried on a strong syntax-driven language such as Java.
Python Lets You Visualize Your Data:
Most Machine Learning and AI developers need to visualize the data from time to time to understand what’s really happening in the code, be it visualizing clusters in K-means or a simple Linear Regression. Visuals are always welcome and many a time even help put your mind at ease by identifying outliers. Python libraries like Matplotlib, Seaborn, and Plotly are excellent help when you want to visualize your data.
Python Has a Growing Community:
Python’s popularity is growing at a rapid pace and the 2020 Developer Survey calls Python the third-most preferred language in the world. As a result of this, there are lots of documentation and support around Python. Python also has a very strong community of developers and sites like Real Python are filled with quality tutorials helping both amateurs and seasoned programmers.
Python uses something called Duck Typing, what it means is, if it looks like a duck and quacks like a duck it must be a duck.
Let’s see an example!
The above code perfectly describes this scenario, as you might have figured out by now, not explicitly mentioning the data type can be useful to the developer at first but can often lead to run time errors.
Moreover, here is how Python spoils you!
It is so simple and straightforward that you eventually feel annoyed working with other languages! God bless someone who swifts from Python to a more structure-oriented language such as Java or even C and C++. All in all, Python seems to go well with almost any Machine learning and Artificial Intelligence project you choose, or up until something better comes our way at least.