Artificial Intelligence (AI) has a wide suite of possible applications. The application being discussed in length here is that of machine learning (ML) — a type of AI that allows computers to learn without programmer intervention or explicit program direction. A common programming language for these practices is Python.

Python is most popular among AI and ML applications for numerous reasons. The first is that it is simple to use, with straightforward syntax. This means it requires less time to gain a comfortable understanding of the basics, allowing those working with AI and ML projects to immediately begin programming and processing large amounts of data.

Another reason for Python’s popularity comes from its established libraries of prewritten code that provide base level functions and actions that are easily applied in AI and ML projects as opposed to developing specific code for specific projects. These popular libraries include panda, Keras, TensorFLow, scikit-learn and more. These same libraries also offer the programmer other data representation tools to create more visually appealing pieces to portray the data with which they’re working. Tools such as histograms, charts and others allow for programmers to display insights and conclusions in a more engaging manner.

In addition, Python’s flexibility yields more possibilities for programmers. Programmers have the option to choose the programming style with which they are comfortable — even being able to combine programming styles in conjunction with other languages to reach the desired result. For example, this is the reason that it can work on diverse platforms including Unix, Linux, macOS, Windows and others. If you’ve been working on a process that you need to transfer to another platform, just modify certain lines of code to make sure everything will remain compatible on the new platform. 

Finally, because Python is an easy language to read, it makes the process of understanding the code, copying the code, or perhaps even changing the code if necessary, a much easier task than other programming languages. This is made even more apparent by Python’s strong community support. Being an open-source language, there are numerous free resources that Python programmers can utilize to ask questions, discuss problems and aid one another. 

These reasons all support Python’s case for the most suitable language for data science applications. The accompanying resource further highlights the advances that Python possesses and how it can be used in AI applications — including ML and data science. For more information regarding Python and its prowess in machine learning applications, please consult this infographic courtesy of Accelebrate.


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Python Programming and Its Importance In Machine Learning

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