Understanding Python’s Capabilities in Machine Learning

In recent years, artificial intelligence and its various applications have revolutionized the the way a number of world-wide industries operate. One example of this is Machine Learning. Machine Learning has become such an important tool in the way that the IT industry operates in addition to being the backbone of many applications people use today. The most surprising thing of all perhaps, is that all of this is made possible without any explicit coding needed. These programs, through the analyzation and interpretation of data, are able to learn and identify possible solutions for companies around the world.

You may not recognize it, but you’re prone to seeing a number of these developments each and every day. Have you ever been recommended to follow someone’s page on Instagram? Or maybe suggested a friend to add on Facebook? Perhaps Amazon has also suggested products you might like based on your previous purchases on the platform. Each of these services are a result of machine learning. Though these may seem simple and of little to no use, there are even more practical instances of machine learning making a difference. For example, automated fraud detection from your banking provider. Translation services, predictive text, and more are all the result of python-powered machine learning developments.

If you’re perplexed as to how any of this is accomplished, you’re not alone. In order for these applications to be developed, it requires a massive amount of data. Programmers then develop code meant to interpret and analyze this data in order to provide business insight that companies are able to use to develop strategy meant to take hold of a competitive advantage in their space. These developments also enhance the user experience as mentioned previously. But what is the most common programming language to accomplish all of this? Python.

The reason Python is the most preferred programming language for such data-intensive applications like artificial intelligence, data science and financial services mentioned previously, is its ability to work so effectively with large and consistently updating datasets. Coupled with the fact that this language possesses a straightforward syntax, it becomes the premier option for those novice programmers that are starting on some of their larger projects for the first time. New programmers, in addition to experienced programmers, greatly benefit from another aspect of Python as well. There are numerous libraries online that contain backlogs of pre-written code useable in any Python project or application for free. This saves programmers a solid amount of time when developing the code they’ll need to work through their projects. Finally, a number of companies offer live online python training courses that teach the language to aspiring programmers.

If you’re still searching for additional information regarding Python’s popularity and importance in machine learning projects, be sure to check out the featured infographic below. You’ll find a number of additional benefits that Python provides to programmers working with Machine Learning applications, in addition to how Python is utilized in Data Science.

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.