As more organizations realize the value that artificial intelligence offers to their many diverse processes, they continue to find unique ways to integrate these technologies into these processes to improve them. One subset of artificial intelligence in particular has taken incredible leaps within the business space, that being Machine Learning. More and more businesses are investing into these technologies in hopes to gain some form of competitive advantage within their industry, but how do these technologies work? With autonomous learning, these machine learning technologies are capable of doing incredible things. This is all made possible through coding and many programmers look to the Python programming language for these projects. This post will detail why that is, in addition to examining the ways that Python has impacted the data science space.
What makes Python so special that it’s the premier language for the job? It’s popularity has to do with a number of factors. The first being that it’s a rather simple programming language to use and learn. With such a straightforward syntax, meaning it’s simple to glance over and read while still understanding, the easier it is for novice programmers to pick the language up. With programmers getting a grasp on the concepts and fine details at a quicker rate, they’re able to build a strong foundation with Python and can feel confident in working with the data they’re presented with for their AI and ML projects.
Part of this aforementioned confidence comes from the many existing libraries of prewritten code that include a number of functions and actions available to be used, free of charge, for any project. This saves a great deal of time for programmers as they no longer have to program base level code from scratch. Some of the libraries utilized include Numpy, Keras, PyTorch, Pandas, Theano and many more. In addition to this prewritten code, these libraries often include data representation tools such as integrated charts and histograms to allow for a more visually appealing presentation of the data programmers are working with.
These libraries are just the start of the support that an open sourced language like Python receives. On top of this support, Python is also extremely flexible, meaning it’s compatible with other languages like C and C++ with a few changes of code. This, along with its ability to work across a wide suite of platforms and operating systems (macOS, Windows, Linux, Unix, etc.) further illustrates its flexibility. This ultimately gives programmers a sense of freedom in programming where and how they prefer when working through any project. All of this makes it clear as to why Python shines as the best choice of programming language for Machine Learning and Data Science applications.
Interested in learning more about how Python Data Science Training courses could make the difference in your organization’s data science efforts? Or perhaps you’d like to learn more about how Python continues to contribute to the many machine learning applications across the world? Either way, be sure to check out the infographic featured alongside this post for more information.
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.