The Strengths of Python In Machine Learning Development

The Strengths of Python In Machine Learning Development

As our understanding of Artificial Intelligence continues to grow, so do the ways we apply the technology. Machine Learning in particular has seen great strides in recent years in regards to the advancements it has made to everyday lives and business operations. But how is this made possible? Machine Learning involves computers inherently learning and interpreting information and trends through large amounts of data – all without programmer intervention. The most common programming language for the development of these systems is Python.

Now, some might be wondering why Python is the preferred programming language for these applications. 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.

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 providing its users with such immense support, matka Python is arguably one of the most flexible programming languages used today. Being compatible with other languages like C and C++, along with its ability to work across a wide suite of platforms and operating systems (macOS, Windows, Linux, Unix, etc.) 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, in tandem with the endless amount of community support due to its open-source nature, Python shines as the best choice of programming language for Machine Learning and Data Science applications.

For more information on the relationship between Python and Machine Learning, check out the featured infographic below.

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. 

Author: Nina Mdivani