Comparing Different Programming Languages For Machine Learning

Comparing Different Programming Languages For Machine Learning

How has machine learning changed the world? Let us have a look at it! You can find it in the recommendation system of amazon, youtube etc. It is used in defence in UAVs (unguided air vehicles) with object detection. It is also used to understand text sentiments on social media platforms in important events like elections etc. All of these things are coded, obviously. And trust me if you are a beginner, with the time you can build such things too. 

And now you may ask which language would really be the apt one to start this journey — because there are numerous languages available for the same. So let us delve deeper into the topic to get a better understanding of the topic.

There are a lot of programming languages which support machine learning libraries, and one may think which one to choose to get the best outcomes for the same. Trying to choose the right language for your own self without any prior information is like being a kid in the toy shop who is confused and fascinated at the same time. So I would advise you to read this article which will give some clarity on the topic. We will also have a look at some libraries that each of them supports for machine/deep learning.

To start with it, first, we need to understand that every language has its pros and cons. So we will be discussing and trying to understand these things really well. 

Let’s first introduce ourselves with some of the languages available for machine learning operations:

  • Python
  • R
  • C++
  • Java
  • Octave
  • Julia and many more.

However, for this article, we are going to compare some of the prominent programming languages — Python; R; MATLAB; and C++.


Python has become the widely used language for machine learning with the most supported libraries for the same purpose. Python’s easy to fathom syntax, inbuilt functions and wide package support has made it a widely accepted programming language as well as the toughest player in the game of machine learning and data science. 

To get a rough estimate about Python’s power, one can simply understand by the fact that we can access over 235,000 packages via PyPI (python package index). Python for everything would really be the right phrase to describe it, and also comes with great community support.

Some of the packages that are being supported in Python for Machine learning are — Tensorflow for deep learning, Numpy for mathematical operations; Pandas for file operations; Pytorch for deep learning package; Sklearn for classification and regression algorithms; OpenCV and Dlib for computer vision; and Matplotlib for data visualisation; to name a few.

With all these benefits in hand, Python also comes with a few shortcomings of being relatively slower than other languages like C++ and also struggles to support multithreading. 


Now as far as building libraries are concerned, C++ comes into the play. We often have heard that C++ has been approached the most when it comes to developing games and large systems. This is because of its portability feature and also provide a basic understanding of the logic building. 

Some of the packages C++ supports include — Microsoft Cognitive Toolkit (CNTK) for deep learning; Tensorflow for deep learning; OpenCV for computer vision; MLPack for machine learning; DyNet for neural networks; OpenNN for neural networks; Shogun for machine learning and FANN for neural networks.

However, just like everything else, C++ has its own shortcomings too. It is very syntax oriented, unlike Python, which is really beginner-friendly. Also doesn’t have great library support like Python.


Other than Python if there’s a language which is used considerably for machine learning and data analysis purposes it is R. It really has become a great alternative, and people have been using it for various machine learning applications. 

R comes with some significant benefit — starting from its good library support and graphs to growing enormously with many for us to seek help from, due to its open nature.

Some of the key packages that are supported by R are — Kernlab for regression and classification based operations; Ggplot for data visualisation; Caret for regression and classification based operations; Plotly for data visualisation; MLr3 for different machine learning workflows; Rpart and SuperML for machine learning; and DataExplorer for data exploration.

Additionally, just like Python, it is also comparatively slower than the rest of the stack like C++. This acts as a massive disadvantage for this programming language. Further to this, it also has a weak origin, making it not-so-easy to learn. People who don’t have a programming background may find it challenging to learn R, which is not the same when it comes to Python.


Last but not the least comes MATLAB — Matrix Laboratory — which supports machine learning operations, and can be innovatively applied to applications including computer vision. To understand it better, we will now share the type of features that are supported by MATLAB.

To start with, MATLAB is not constrained with syntax and thus is easy to learn and understand. MATLAB also has a lot of predefined functions and GUI for the learners to understand things better. Additionally, it comes with a MATLAB compiler which helps when it comes to coding in the same.

Furthermore, when it comes to machine learning capabilities, MATLAB supports it in a unique way. MATLAB allows users to apply AutoML, which enables users to make the most of the optimised and reduced coded models. Along with it, MATLAB can also perform automatic code generation for sensor analytics and many more.

Similar to other languages, MATLAB comes with some constraints too. The programming language is not readily available or free to use. In fact, a trial version is available only for 30 days and post that the users need to buy the package for further usage. Because of this reason, one may find only researchers to be its target audience. Not only that, MATLAB compiler is costly to buy and therefore it doesn’t have a dense community/forum support to help the users and learners.

The Right Comparison Of Programming Languages

Now you may ask if everything has pros and cons, which one would be the best, to begin with. So for that, one needs to understand the purpose. If one is into developing packages for machine learning or game development, then C++ may really help you a lot. One the other hand, if one is into the research-oriented sector and doesn’t know much of coding, then MATLAB can surely help. 

Python and R, on the contrary, stand with each other really close when it comes to machine learning. Both of them have great support, and learners can also get some online help in the learning path if needed. With such arguments in hand, Python is supported more because of its ease to code. R, however, has some complicated constraints which someone who’s not into coding may not get. 

So if I am supposed to say, Python, in today’s dynamic market, is sure to persist the roots into the same for at least five years and would be good to go with. But at the same time, it doesn’t mean you should not try other languages. Tastes and preferences may differ from person to person. 


This article was aimed at discussing a few languages which are/are emerging as the leaders in machine learning. In this article, we discussed various languages, their pros and cons and how to get an intuition to go ahead with the right and most suitable programming language.

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Bhavishya Pandit
Understanding and building fathomable approaches to problem statements is what I like the most. I love talking about conversations whose main plot is machine learning, computer vision, deep learning, data analysis and visualization. Apart from them, my interest also lies in listening to business podcasts, use cases and reading self help books.

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