Companies worldwide have adapted to the recent wave of artificial intelligence applications in some unique ways. Most of these ways have improved their operations and internal processes a great deal. For example, many organizations have looked to Machine Learning, an artificial intelligence known best for its ability to learn autonomously. These programs are capable of learning strictly based on the data that businesses are inputting into them. How is this made possible, though? A number of talented programmers utilizing the Python programming language.
But why Python? Python’s popularity amongst Artificial Intelligence and Machine Learning Applications is due to a number of reasons. The first reason is that it’s a rather simple programming language to use and learn. This is in part due to its straightforward syntax, meaning it’s not a difficult language to read and understand. This allows programmers to get a grasp on the concepts and develop a strong foundation with Python and are immediately able to begin working with the data they’re presented with for their AI and ML projects.
Being beginner friendly isn’t all Python has going for it, though. In fact, most would argue that the most beneficial aspect of the language is the pre-existing libraries full of pre-written code for programmers to implement and utilize when necessary. TensorFLow, Theano, scikit-learn, and many more, provide base level functions ready to be integrated into whatever project a programmer is working on. So, rather than having to spend the start of each project fleshing out the basics, they’re already provided. An effective head start, if you will. In addition to this pre-written code, these libraries also offer programmers free tools meant to better graphically represent the data they’re working with, as well as the analysis that they conduct.
In addition to these resources, Python is one of the most flexible programming languages that can be used for these applications. Programmers have more options when working with Python in this case as it is able to be combined with other languages in order to reach the desired result. For example, Python is able to be used on all major operating systems. Unix, Linux, macOS, Windows, whichever is preferred or necessary for the task at hand. Alternatively, if you’ve been working on a process you need to transfer over to another platform, not a problem. Some simple modifications of the code will ensure that it will run just as smooth on the new platform.
If these reasons weren’t enough, rest assured you’ll find even more information regarding Python’s capabilities in the Machine Learning space within the infographic accompanying this post. In addition to this, some more information regarding Python and Data Science is included as well.
Author Bio: Anne Fernandez – Anne joined Accelebrate in January 2010 to manage trainers, write content for the website, implement SEO, and manage Accelebrate’s digital marking initiatives. In addition, she helps to recruit trainers for Accelebrate’s Python Training courses and works on various projects to promote the business.