For any aspiring Data Scientist, it is a shared desire to have the best tools at one’s disposal to get the most out of the time, one invests into knowing or studying Machine Learning. So if you are one of them, we will help you out by answering your question to “What is the best Programming Language for Machine Learning?“
Machine Learning is a subset of AI that enables a computer to automatically learn from observations and real-world interactions translated in the form of data and information. A computer would thus be able to improve upon the results it produces without being explicitly programmed to do so. It thus forms a quintessential basis in an AI system in enabling it to behave intelligently.
We would also briefly discuss where each of those languages is commonly used or the applications where they find the most utility. Thus, by the end of this article, you should have a good idea for the language you must learn to code in for the specific area of your interest, such as data visualization, robotics, etc. Furthermore, we will also give you a long answer to your question and a short one. However, we recommend going through the long answer since it would naturally be more informative and give you a sense of what might come in handy to you for your specific area of interest, instead of a generalized overview.
In short, the language most commonly used for Machine Learning applications across the board is Python. Its tremendous popularity in the field is owed to its syntactic simplicity, making it relatively easy to learn or implement.
Python was created in 1991 by Guido van Rossum. It is a general-purpose, high-level, and open-source programming language supporting object-oriented, imperative, functional, and procedural development paradigms. It is unanimously considered to be at the top of the list of all languages used in Machine Learning due to its simplicity. Python has a straightforward syntax that can be mastered by anyone and serves the added function of cutting down the development time compared to other languages such as C/C++, Java, or Ruby.
Python also owes a great deal of its popularity in the field to its excellent libraries available to help you in all sorts of ML endeavors. These libraries are created by various experts in the industry. For example, the Numpy library, created by Travis Oliphant, is widely utilized for multiple scientific computations, the Scikit-learn library created by Google employee David Cournapeau, is extensively used for all kinds of Machine Learning functions, and the list goes on.
Python is used virtually everywhere, from beginners to advanced professionals, and possibly in every industry that has to do with Data Science or Machine Learning. However, it is limited when it comes to features such as low-level optimization or security.
C++ is possibly the oldest language in this list, and there is a high probability that it’s also the first programming language you have learned. The inception of Bjarne Stroustrup’s work in creating C++ occurred in the year 1979. Most Machine Learning platforms support C++, including TensorFlow. TensorFlow is a Machine Learning library developed by Google that can perform all sorts of Machine Learning functions, from training the simplest of models to running deep Neural Networks. TensorFlow’s C++ API is concise and straightforward, providing smooth graph operations, including easy specification of names, device placements, etc. All of which can be implemented with just a few lines of code.
C++ also provides excellent low-level optimization, with direct control over memory, making it easier to optimize one’s data layout. Furthermore, it allows a programmer to delve deeper into specifically tuning for one’s CPU, data, parallelizing independent processes over multiple threads, etc. However, it does have its caveats. It is relatively more complicated in terms of its syntax and implementation and poses a relatively more significant challenge for someone new to C++. Naturally, it also requires a longer development time. Nevertheless, in applications where performance is the main focus, C++ generally reigns supreme.
Where is C++ used in ML or AI? For Artificial Intelligence in games and in Robot Locomotion applications, C++ is preferred over the other languages. This is due to the low-level control, efficiency, and high-performance that these applications demand that makes low-level programming languages such as C++ with sophisticated AI libraries a brilliant choice.
Java is probably the most popular programming language in the world. Naturally, it has found significant utility and support in the field of Machine Learning and Artificial Intelligence. Java is relatively easy to learn compared to C++ and is generally the go-to language for security and user-experience related applications. Java provides simple debugging processes, vast package services and is generally better suited for larger projects. It also includes Swing and SWT (Standard Widget Toolkit) that allows for more appealing and sophisticated graphics and interfaces.
Java 11 also brings improved features such as improvements in the String class with new String methods. It also features additions and improvements in file handling and pattern recognition.
- New String methods: isBlank, lines, repeat, stripLeading, stripTailing, and strip
- New File methods: isSameFile, WriteString, ReadString
- Pattern Recognition methods: asMatchPredicate, etc.
It thus comes as no surprise that Java is widely used for Network Security applications in safeguarding against cyber attacks and Fraud Detection where Python, in fact, finds minimal utility.
Java also has a significantly long list of libraries which can be used for various Machine Learning purposes. A perfect example is the JavaML library, which provides a collection of Machine Learning algorithms implemented in Java. ADAMS, which stands for Advanced Data Mining and Machine Learning Systems, is another third-party open-source library worth mentioning.
R is a graphic-based, dynamic, object-oriented, and functional language that first appeared in 1993 but has gained popularity in the past few years amongst Data Scientists and Machine Learning developers for its functional and statistical algorithm features and its highly popular features regarding data visualizations. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand.
Naturally, due to the features mentioned earlier, it is a popular tool for Data Scientists and Statistical Engineers. It is also used for Machine Learning functions such as Regression, Classification, and Decision Tree formation. It has found great utility in the fields of Bioengineering, Bioinformatics, and Biomedical Statistics. It is often wrongly compared to Python as both differ significantly in terms of their scope, library support, popularity, and areas of utility.
Conclusion
Thus, it could be concluded that there is no hard and fast “Best Programming Language for Machine Learning.” Instead, what years of development by experts have created is a large set of tools, each having its unique traits, readily available to be used by anyone interested. Thus, it solely depends on what you want to build or whichever language you feel is more natural for you to learn or master. Nevertheless, it is not uncommon to find developers porting languages they are already familiar with into Machine Learning.
However, if this is the first time you are coding, we would strongly recommend you to try Machine Learning using Python as it has excellent libraries and support and straightforward syntax that should get you up and running in no time. On the other hand, Java is the most popular language when it comes to enterprise applications, and if that is your goal, you should acclimatize yourself with Java. Similarly, C++ is excellent for performance-focused applications, and so on.
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