Machine Learning is one of the most dynamic fields and it is a subset of AI (Artificial Intelligence ). Based on this Machine Learning machines can make predictions based on some experience or based on some data. There are various programming languages are available that can be used for machine learning but Python is the most prevalent language used in machine language. And here in this article, I will mention some of the essential reasons why python is so popular in machine learning? So please read this complete article for more details.
If I speak about why Python is so prevalent in machine learning? In short, then I should say that it is a very popular language and very easy to use. And besides that, the readability of this programming language is very good and there are numerous libraries and functions attached with Python which you can use for any kind of coding errors or coding problems. These are some common causes that most Developers for companies use Python for machine learning.
Besides these factors, there are other constituents also available and in the below section I have mentioned all those factors one by one. So please stick with us.
5 Reasons about Why Python is so Popular in Machine Learning?
Easy to Use :
The first essential reason why Python is so popular in machine learning is that it is very comfortable to use in comparison with other programming languages.
It generally means that the syntax of the Python programming language is very simple and very easy.
If you have some knowledge about any kind of Computer programming then you can undoubtedly understand all the concepts of python.
And you can discover this programming language very quickly and within a very short period within one month to 3 months.
Because of its simplicity, most developers and corporate companies ratify this programming language for their research works and their development work.
Short Code :
This is another good advantage of using the Python programming language.
With the help of this programming language, you will be able to implement your code within short lines, In comparison with other programming languages.
If I provide an example then within Java programming language if you write 200 words or 300 words lines of codes then the same work you can do within Python programming within 60 or 70 words lines of codes.
Libraries and Frameworks:
Being an open-source programming language Python has several hundreds of libraries which further consist of numerous frameworks.
And all these frameworks are beneficial for the developers to complete their work.
NumPy, Pandas are some of the libraries that a Python developer uses.
- NumPy: It is generally used to create multi-dimensional arrays And besides that if you want to perform various operations with arrays then you can use this NumPy library.
- Pandas: This library helps you to read data from different kinds of sources. You can efficiently retrieve the data. And then with the help of data series you can do lots of operations.
- Seaborn: This library includes some of the most statistical analyses So you can visualize the data set Along with that you can also perform statistical analysis On that specific data.
- Scikits Learn: In this library most of the machine learning algorithms are present. So if you want to implement any kind of machine learning algorithm Then you can call that and use it within your problem statement.
Multiple Functionalities :
Besides these things, Python has multiple functionalities and you can use it within several areas.
It is used to create web-based applications Web Services Web scraping and several other things.
So these functionalities make Python much more familiar to use in machine learning and other areas also.
Community support :
Many developers are using Python skills to increase their machine learning knowledge.
Apart from that, they are also using python for implementing various deep learning projects.
If you go into Google and search about this then you will get various resources such as GitHub links, YouTube videos, blogs, and website articles Where machine learning is explained from basics.
And this all happens because of the Python programming language.
Many top companies like Google, Facebook, Amazon, Basically use python to implement Most of their products.
Is Python Necessary for Machine Learning?
Well, there are several programming languages that you can use for Machine Learning.
Some programming languages like – Java, Scala, and some others.
But in comparison with Python, these languages are not so compatible with Machine Learning.
Numerous libraries are present within Python Programming and these are helpful for Machine Learning.
And these libraries and features do not exist within other programming languages.
Therefore I should say that it is not mandatory to learn Python programming for Machine Learning but it is the most preferred programming language used for machine learning.
Scope of Python in Machine Learning
There are various scopes available for Python in Machine Learning. Because it is the most demandable programming language used for Machine Learning.
And this programming language is not only used for Machine Learning and besides that but it is also used for Artificial Intelligence.
Python is also used for several other areas, so the scopes of this programming language are very much higher than the other programming language.
Advantages of Python in Machine Learnin
- Availability of various frameworks.
- It is very useful.
- It has very good community support.
- The readability of this code is very good and very clear.
In this article, I have mentioned some of the essential causes and advantages of using Python programming within Machine Learning.
But besides that here i also want to state that it is not the only programming language within Machine Leraning but besides that there are other several programming language are available such as – java , Scala which are also useful for Machine Learning. But Python is the most popular one in this field.
I hope you have liked this article and if you have any kind of inquiry then you can ask me in the comment section.
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