Industries worldwide continue to benefit from their adaption of artificial intelligence and its various applications. One discipline in particular has made a huge impact, Machine Learning. Not only has machine learning impacted the way in which the IT industry operates today, it also serves as the backbone of countless features of applications that people use every day. What most people don’t know about machine learning, though, is that it’s made possible relying mostly on inherent learning made by computers themselves rather than from programmer intervention. The insights that these programs provide help companies around the world identify competitive advantages in their industries.
A majority of the most popular features on social media are a result of machine learning. For example, think of how many social media platforms that have some form of user recommendation. Facebook friend suggestions, Instagram account recommendations, a ‘Twitter users you might know’ section. Data used by machine learning algorithms are responsible for these suggestions. The same can be said for any online web-store product recommendations. Amazon, Walmart and the all other major online retailers utilize machine learning to provide the most personalized experience for customers. Even more complex work is accomplished through machine learning. Take the fraud detection services from any major banking provider for example. All of these and more are accomplished through python-powered machine learning programs.
But just how are these programs developed? How are they supported? First things first, none of this would be possible without enough data. The constant collection of pertinent data is of utmost importance to making these programs successful. Once data collection is settled, programmers can develop code focused on interpreting and analyzing the data and translating it into actionable insights. These insights are then used to develop the business strategies meant to provide some form of competitive advantage. The programming language used most commonly in machine learning is Python.
With such data-intensive applications like the ones mentioned above, Python becomes the most obvious programming language of choice. Namely because of its ability to work so efficiently with massive amounts of data. The language itself benefits from a number of qualities as well. With such a straightforward syntax, it’s one of the first programming languages that beginner programmers are taught meaning it’s very common knowledge in the engineering industry. Not only is it beginner friendly, Python also benefits greatly from its community support. With countless free libraries full of pre-written functional code useable by any programmer, Python projects become a bit easier to manage. These libraries save programmers countless amounts of time when developing the code necessary to work through Machine Learning projects. There are even companies that offer live online python training courses for programmers hoping to learn more about the language.
Python’s popularity doesn’t end there, though. There’s still a lot more information regarding its importance in machine learning to be discovered. For more of that information, be sure to check out the infographic coupled 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.