A Deep Dive Into The Ways Python Programming Is Improving Machine Learning Applications

A Deep Dive Into The Ways Python Programming Is Improving Machine Learning Applications

While it’s true that Artificial Intelligence (AI) possess a wide range of capabilities across a spectrum of applications, the one that this post will primarily focus on is machine learning. This form of Artificial Intelligence is what allows computers to learn from data on their own, without the help of any programmer. When considering how these machine learning miracles are programmed, the primary language is Python.

What makes Python the first choice for programmers? Of the many, one of the most simple ones is that Python is so straightforward to use, with simple syntax. Which means it takes less time to learn the language so programmers otherwise unfamiliar can familiarize quickly and begin working with large amounts of data comfortably.

With its simplicity also comes a huge list of additional external resources, offered primarily by the Python community. One of those resources are libraries of prewritten code that provide base level functions and actions that are easily applied in AI and ML projects. These popular libraries include panda, Keras, TensorFLow, scikit-learn and more. These same libraries also offer the programmer other data representation tools to create more visually appealing pieces to portray the data with which they’re working. Tools such as histograms, charts and others allow for programmers to display insights and conclusions in a more engaging manner.

The list goes on as far as the benefits of Python. For example, its flexibility means programmers have more options. They can choose the programming style with which they are comfortable — even combining styles and using Python with other languages to reach the desired result. Further evidence of this flexibility is the fact that it can work on diverse platforms, including Unix, Linux, macOS, Windows and others. If you’ve been working on a process that you want to transfer to another platform, it’s relatively simple: Modify certain lines of code to make sure that the code will work in the new platform.

Sometimes the development of these programs and applications can be hard for those unfamiliar to understand. However, with Python being such an easy language to read, it makes the process of understanding the code, copying the code, or perhaps even changing the code if necessary, a much easier task than other programming languages. When it comes to presenting the findings of what these programs can accomplish, this can make all the difference in making it more understandable for those unaware.

These reasons all support Python’s case for the most suitable language for data science applications. Many businesses would actually benefit from the help of third-party offered Online Python Training Courses as they can make a significant difference in the way their operations utilize Python amongst their projects. For more information on the types of courses offered, be sure to consult the infographic featured 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.