From Source to Machine Language For decades, the goal of a business organization has been to be able to describe a problem and have it turned into executable code (machine language).

All programming languages have their proponents, but not all are equally equipped with libraries for data science and machine learning. (Image: Igor Stevanovic, Getty Images/iStockphoto) ...

A programming language lets the programmer express data processing in a symbolic manner without regard to machine-specific details. See how many programming languages are there?.

Python, meanwhile, is an interpreted language, which means that its human code is converted into machine code line-by-line as the program executes, enabling it to run without first being compiled.

Kamalkumar Rathinasamy leads the machine learning based machine programming group at Infosys, focusing on building machine learning models to augment coding tasks.

Not necessarily for the data-science and machine-learning communities built around Python extensions like NumPy and SciPy, but as a general programming language.

This is why I’ve long argued that BASIC is the most consequential language in the history of computing. It’s a language for noobs, sure, but back then most everyone was a noob.

The future of software development isn’t just about who codes faster—it’s about who can express intent more naturally.