Machine learning has rather been defined by Andrew Ng, a computer scientist at Stanford University, as being “the science of getting computers to act without being explicitly programmed.” It was actually first conceived in the 1950s but experienced limited progress until around the turn of the 21st century. Since then, machine learning has been a driving force behind a number of innovations, most notably artificial intelligence.
Several different industries are indeed already benefiting from machine learning, and there is also an increasing demand for ML products as well as services across the developed world.
Businesses of all sorts are indeed taking advantage of its predictive capabilities, and also to seek to develop prescriptive machine learning methods in order to make informed decisions. There are many different ways for companies in order to approach this technology, including several programming languages that do stand out in the field accessible to the general public.
Python happens to be a dynamically typed language, which can also create problems in machine learning environments. For one, errors can become difficult to track as the program does grow larger and more complex. This can also create costly drawbacks and also slow down performance.
Also developed in the early 1990s, R happens to be a part of the GNU project. It is also widely used in data analysis and is typically applied to common machine learning tasks such as regression, classification and decision tree formation. It is also a very popular programming language among statisticians.
R is also open source and is also widely renowned for being relatively easy to install, configure and make use of. It is platform agnostic and integrates well with other programming languages. Along with data analysis, R has particularly strong data visualization capability.
Despite rather being relatively easy to integrate with other tools, R has some unique quirks that can make it somewhat confusing to learn, such as its unconventional data structures and indexing (which starts at 1 instead of 0. It also is less popular than Python and thus also does not have as much documentation available for machine learning applications.
JavaScript
Developed in the mid-1990s as a tool in order to improve on web development practices, JavaScript has no doubt since become one of the most widely utilized languages in that field. It is a high-level and dynamically typed language that is flexible and multi-paradigm. Although its applications in machine learning have been rather limited, high profile projects such as Google’s Tensorflow.js are based on JavaScript.
One of the most promising features of JavaScript in the field of machine learning is that it does open up opportunities for web and front-end developers, who are largely already well acquainted with it and thus have an accessible point of entry into an otherwise somewhat obscure as well as difficult niche.
As it does exists now, however, the ecosystem for machine learning with JavaScript is still somewhat immature, so support for this type of development is indeed currently limited. It also does lack the range of functionality for data science that languages like R and Python already have built into them.
C++
Among today’s most common programming languages, C++ is of course probably the oldest. Developed at Bell Labs in the early 1980s, C++ emerged out of doctoral research that sought to extend the C language. Enabled with both low and high-level programming
C++ works especially well for resource-intensive applications, which is part of what makes it indeed great for machine learning. And as a statically typed language, it can also execute tasks with relatively high speed.
However, C++ does require a great deal of complex code in order to build new applications, which can be highly time-consuming and can cause a great amount of difficulty in maintenance. This can also make it very easy for beginners to create errors
However, C++ does require a great deal of complex code in order to build new applications, which can be highly time-consuming and can also cause a great amount of difficulty in maintenance. This can also make it very easy for beginners to create errors.
Java
Developed by Sun Microsystems in the mid-1990s, was originally built to be high-level and is an object-oriented programming language that does look and feel similar to C++. Along with being extremely popular, Java can indeed implement a wide variety of algorithms, which are quite very useful to the machine learning community.
Java is of course regarded as one of the most secure programming languages, largely due to its use of bytecode and sandboxes. Java does manage to harness much of the power of C++ while overcoming its security issues and overall complexity.
But with all of its improvements over C++, Java has a reputation for being slower than many other programming languages.
Additionally, as of 2019, Java has rather implemented commercial licensing for certain business applications, which can indeed be costly.
Conclusion
Of all the programming languages being applied to machine learning, Python remains the most popular. Nevertheless, languages such as JavaScript could indeed likely grow in popularity as the landscape changes over time. And although human programming will never indeed go extinct (or at least not any time in the near future), programming for machine learning which will likely become less focused on code in coming years, as machines are trained to code themselves.
