It’s hard to deny the impact that Artificial intelligence (AI) has had on the evolution of industries worldwide. As more and more organizations have begun identifying ways in which AI can aid in acquiring a competitive advantage, their operations have drastically changed. With such a wide suite of applications, AI continues to find usefulness across even some of the least expecting industries. Of its suite of applications, this post will focus on one in particular: machine learning (ML). This form of artificial intelligence is incredible in that computers become capable of learning and identifying patterns on their own as a result of collected data. Autonomous learning, all without the help of a programmer. Well, sort of. A great deal of code goes into making all this possible and there is no language more suited for the job than Python.
What makes Python such a clear favorite for developing these machine learning applications? While there are a number of clear-cut advantages to using Python, it certainly isn’t the only language used. However, due to its simplicity, it is a very effective language to use. With such a straightforward syntax, even those unfamiliar with the language will have an easier time becoming acclimated, which means businesses can feel confident when assigning new employees to AI and ML projects that may have little to no experience in the language. Python is also able to process large amounts of data effectively, which gives it an edge as well.
While Python has some fundamental qualities that make it easy to pick up as a programmer, it also benefits from an extensive collection of online libraries available to the public, that are totally free and include basic prewritten code ready to be used for specific functions and actions. This saves programmers a great deal of time through importing basic code rather than spending their own time coding it. Popular libraries used on a daily basis include scikit-learn, TensorFLow and Keras. Many of these libraries even include free tools to help programmers present their findings in a more professional manner such as histograms or varied chart options.
As mentioned previously, Python certainly isn’t the only programming language people use to work on these AI and ML projects. With that being said, Python’s flexibility allows it to bend to the ebb and flow of the programming process. Whether that be alongside other programming languages or having to operate on a completely different operating system, Python is capable of functioning with a few tweaks when necessary. With guaranteed functionality on macOS, Windows, Unix and Linux, any programmer can reliably turn to Python with just a few modifications to their code.
With many Python projects being open source, any novice programmer, or even expert programmer, can receive value feedback from their peers. With such strong community support, there are plenty of networks out there for programmers to bounce ideas off one another and compile code. This also means the number of external resources are vast, which can help programmers make the most of the projects they work on when they’re stumped.
Python’s capabilities make it a clear favorite for developing data science applications. Some of the strongest uses of both AI and ML were accomplished through the use of Python code. To learn more about the ways in which Python can continue elevating your organization’s AI and ML projects, consider seeking help from certified professionals through Online Python Training Courses.
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.