With more and more programmers working toward polishing the way that their businesses utilize artificial intelligence, the ways in which it is applied to their processes also expands. While artificial intelligence as a whole continues to progress, one aspect under the umbrella specifically continues to impress: machine learning. This form of AI allows computers to learn through the analyzation and interpretation of data without any explicit programmer intervention or code. Not only is this work done autonomously, but it also provides some incredible analysis and interpretation of a company’s data, to the point they’re able to learn and identify different possible solutions for the many complex problems they face on a daily basis.
Nearly every application offered by businesses worldwide have made some adaptation to these advancements. For example, how often are you recommended to follow someone’s page on Instagram? What about the number of accounts piling up on your recommended friends on Facebook? Even massive retail operations like Amazon utilize these technologies through suggested products based on 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.
What makes these processes possible, though?. A majority of these possibilities first require massive amounts of data input to be assessed. Once the data has been assed, programmers can develop the code necessary to break down the data into more meaningful and interpretable insights that companies are then able to use to develop business strategies in real-time. The coding language that makes all of this possible is Python.
Python’s strengths within the artificial intelligence space, specifically related to data science and the financial services mentioned previously, is its ability to work so effectively with large and consistently updating datasets. Its effectiveness is in part due to possessing a straightforward syntax, it becomes a language that’s taught across most novice programmers. 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 python data science training that teach the language to aspiring programmers.
While it may seem clear that Python is the premier choice for these data science applications throughout the many industries across the world, there’s still much more to learn. For more information on how these advantages play a part in the way that businesses operate today, be sure to read on to the infographic coupled alongside this post: Python Programming & Its Importance In Machine Learning.
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.
