Artificial intelligence (AI) has provided a means for organizations all around the world to gain some form of competitive advantage in their industries. This is largely in part due to the wide suite of applications that Artificial Intelligence encapsulates. One of the most prominent today is machine learning (ML), a type of AI that allows computers to learn without being explicitly programmed or requiring programmers to intervene. While other languages may be used, the most popular programming language for machine learning applications is Python. There are a number of unique attributes that make Python the clear-cut choice for developing machine learning applications. The first being Python’s simplicity. Python has a straightforward syntax, which means, on average, it takes less time to learn the basics of the language. This means that programmers can jump right into working with the large amounts of data they have to analyze. Python is also an easy language to read and thus easier for beginners to understand what the code means as well as copy and change it, if necessary. Beginners are also met with a strong sense of community support. Python is an open-source language which means there are numerous external resources that Python programmers use to ask questions, discuss issues and help one another all free of charge. While these are some of the basic advantages Python has in this space, there are many more to learn about. For more information on those strengths, be sure to check out the resource supported alongside this post.
Python Programming & Its Importance In Machine Learning provided by Accelebrate, a company offering courses in Python Data Science training
