More and more organizations worldwide are adopting different artificial intelligence applications into their processes to significantly improve their efficiencies. Of the most common, many machine learning applications stand out above the rest. This form of artificial intelligence is capable of autonomously learning with little to no programmer intervention once fully fleshed out. This can clearly lead way to a number of improvements in the way that many companies operate. Before these technologies can make their mark, they first must be developed and the programming language responsible for a number of these applications is known as Python.

While it’s true that many languages can support these machine learning technologies, Python is a sound choice for a number of reasons. First and foremost, Python possess a simple and straightforward syntax. Meaning it’s one of the easiest languages to understand even without any experience with it. Which is why it’s so commonly taught as the first language for many aspiring programmers around the world. Knowing that mastery isn’t required in order to begin working with the data these systems are meant to interpret, it becomes an easier language to pick up on in a short period of time.

While its simplicity does help build its case as one of the most supported languages for these applications, one of its other large benefits is the numerous amounts of established libraries full of prewritten code for base level functions and actions that programmers can easily extrapolate for their AI and ML projects rather than developing this code from scratch each time. Some of the most popular libraries include: Keras, TensorFLow, scikit-learn, in addition to many others. These libraries often include additional functions such as data representation tools like charts or histograms in order to more properly visualize the analysis they’ve worked through.

Python’s simplicity paired with its flexibility also make it an easy choice for the programming language of the many machine learning applications out there today. Programmers have more options when working with Python within this space as it is able to be combined with other languages with ease to reach necessary goals. For example, Python is compatible with nearly all major operating systems with a few tweaks. 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.

While it’s clear that Python has its place in the machine learning and data science space, it takes some time to master. To learn more about the different types of Python Data Science Training programs available to help your organization reach its goals, in addition to more ways that support Python’s stake in the space, be sure to check out the infographic featured alongside this post.

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.