Ever consider the ways in which technological advancements such as machine learning are contributing to your life? GPS services, for example. Looking to get where you’re going faster and more efficiently? Popular services such as Google Maps can help make sure users are on track and on time by leveraging machine learning to continually assess traffic conditions. How about the ways it’s shaping the social media platforms you use regularly? Facebook is one example of ML in action — Meta leverages a host of machine learning tools and libraries to help users connect. Python helps underpin all of the efforts mentioned above, and many more. While the value of these machine learning applications may have become more clear, what isn’t clear is how they’re developed. Many of these machine learning advancements are founded on Python as it is often the preferred choice of programming language in this space. Why is that? Python is open-source, which means it’s free to use and available for any purpose. This makes it a great choice for companies building ML solutions since they don’t need to pay potentially expensive licensing fees. In that same open-source vein, the Python community has more than 15 million members worldwide, putting it second only to JavaScript. For machine learning developers, more coders working with Python means more available options to use in ML algorithms, rather than building them from scratch. This is seen even more evidently in the host of Python library options to help companies build and manage their ML applications. These are just a few of the ways in which Python demonstrates its prowess in the machine learning space. To learn more, spend a moment reviewing the resource coupled alongside this post.
Python Programming & Its Importance In Machine Learning provided by Accelebrate, a company offering courses in Python Data Science training
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