Machine learning and artificial intelligence are huge buzzwords in today’s day and age, but what considerations need to be made by technologists attempting to take a deep dive into it?
Fundamentally, machine learning revolves around the notion of “teaching” a machine to make informed decisions based on “past experiences” as defined by data. To do this successfully, massive amounts of data need to be processed by extremely sophisticated algorithms. When it comes to choosing a programming language to execute this processing, Python becomes a top contender for different reasons.
Python is Simple
Being a high-level programming language, Python’s syntax is extremely similar to the English language in contrast to low-level programming languages. This offers users a more simple and intuitive language that does not require much effort to implement by beginners and experts alike. Readability is at the very core of Python with its utilization of spacing over end-of-line characters like braces, making it extremely easy to collaborate and contribute quickly to projects.
The Python community contributes to its simplicity as well. Its extensive user base led to the myriad of Python resources that exist for users to learn new features and best practices, meet fellow technologists, and troubleshoot. Furthermore, Python is backed by such extensive documentation that it does not take much to get a sample application up and running.
Python is Flexible
The dynamic nature of Python makes it extremely resilient. Instead of focusing on data type specification, for example, developers can simply declare necessary objects or methods while letting Python do all of the heavy lifting. The fact that Python was not created to solve an extremely specific problem makes it applicable across various scenarios as well.
Python is an open source programming language, making it free for public use and open to new contributions or modifications of existing features. This makes Python extremely powerful as it combines the knowledge and expertise of technologists around the globe to bring extremely rapid fixes and new functionality.
Python is Efficient
The widespread popularity and use of Python, both in and out of machine learning or artificial intelligence, has contributed to the vast number of libraries and frameworks that exist for the Python community to use. Instead of getting bogged down with reinventing the wheel, programmers can leverage such libraries and frameworks to perform common tasks and focus on the new functionality of their service. Not only does this save time and effort, it reduces time to market drastically.
Being an interpreted language, Python source code is converted to byte code (to be understood by a machine) using an interpreter. Interpreters read and execute code line by line, completely independent from the platform on which the code is running. This makes it extremely quick to test and debug code across different environments. Python’s memory management also makes it efficient within the context of memory, with mechanisms to facilitate garbage collection and ensure each Python program has adequate memory allocated to it — all done by the Python interpreter.
Of the many programming languages that exist, Python presents many strengths that make it one of the most popular languages for solving machine learning and artificial intelligence problems. Its simplicity, flexibility, and memory management all shine in the face of heavy computation and processing required in machine learning and artificial intelligence. Python’s ability to ingest massive amounts of data, create visualizations of that data with ease, and process the data to train the machine as desired makes it a natural choice in machine learning and artificial intelligence applications.