Why Python for Machine Learning

Updated: May 14

In this blog, I will tell you why python is used in large number than any other programming languages in machine learning and some features of Python.


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As AI and ML are being applied across various channels and industries, big corporations invest in these fields, and the demand for experts in ML and AI grows accordingly. Python is the most popular language for AI and ML and based it on a trend search results on internet.


Simple and Consistent


Python is simple language offering reliable code. Machine learning is all about complicated algorithms and versatile workflow. So the simplicity of python helps the developers to deal with complex algorithm. Also it saves the time of developers as they only require concentrating on solving the ML problem rather than focusing on a technicality of language.

python is easy to read programming language for humans. Moreover, the developers learn this language with ease. They are comfortable with coding in python and building models quickly for machine learning.

The involvement of multiple programmers in the same project may create issues in other languages. But the Python is considered the best choice for collaboration when many developers are working on the project. 


Flexibility 


Python is known as most flexible language in machine learning. It provides various options for users. The flexibility factor reduces the possibility of errors. It let the programmers take the situation completely under control, and work on it comfortably.


Libraries and Frameworks


Developers require a well-structured and well-tested environment to develop the best coding solutions. The ML algorithms are very complex, but Python is the rescue with an extensive range of libraries and frameworks.


A library is a prewritten code that is used by developers during a common programming task. These libraries help the programmer and reduce development time. Python has a rich technology stack and has a different set of libraries for Machine learning.


  • Keras, TensorFlow, and Scikit-learn for machine learning

  • SciPy for advanced computing

  • Seaborn and Matplotlib for data visualization

  • Pandas for general-purpose data analysis

  • NumPy for scientific computing and data analysis


Platform Independence


Platform Independence reflects the versatility of a programming language. It refers to the framework or programming language which allows the developer to implement things on one computer and use them on another.

Python is a platform-independent language. It is supported by many platforms, including Windows, LINUX, and macOS. Also, the Python code creates executable programs for any standard operating system.  


Moreover, these systems will not require Python interpreter to use or execute the code. Also, it reduces the cost of ML models training and saves your money.




Python has become the first choice of programmers in machine learning. The services of Python are suitable for ML developers. If you are developing software in ML, then use Python. The credibility of this language is higher than in others. Also, it is easy to use and understand. So it has become a popular choice for ML  

The Python code is suitable to use and implement on any platform. Also, flexibility plays a vital role in Python. I have shared my views about the use and the benefits of Python in ML. And I hope that this post has helped you in getting a clear image of Python's role in ML.

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