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Which Programming Language Is Considered To Be Best For Machine Learning?

These are a bulk of people pondering the same question and exploring different answers but at a hanging stage not knowing which one is correct and which one should they follow. The answers are very conflicting some share it on the basis of the research while some share their personal experience which confuses the newbies as hell. Well, the answer to this question depends on what you are trying to develop and why have you indulged in developing a machine learning app in the first place. I know the answer is a diplomatic one.

But, let me tell you one thing most of the popular development languages are adding their support to simplify the machine learning. This is because of its high adaptability. When we think of machine learning, four basic languages comes to the mind:

  • Python

  • R-Programming

  • MatLab

  • Octave

Let’s conduct a comparative study on the factors affecting the machine learning.

1) Speed

While choosing the best programming language, speed is an essential thing to consider. R was basically built as the statistical language. This means that it has higher data-analysis and statistical support. On a contrary, Python depends on the packages. Hence, when it comes to tasks related to statistics, R has an upper hand compared to the Python and is a bit faster. So, if your machine learning project has to be rapid, R programming is your choice.

2) Learning Curve

When it comes to functional perspective, R is the programming language. Whereas when it comes to being object-oriented, Python is the language. If you belong to a functional programming background, learning Python would be a lot easier as compared to R. Coming to the Octave and Matlab both are similar to writing some mathematical equations and yes again easy to learn and implement.

You can’t really pick any one programming language for machine learning. It depends on your technical background and experience as to which language will be easy to learn for you.

3) Costing

The only language which is paid and needs a license for its use is Matlab. The other three preferred languages for Machine Learning are open source and is totally free for the use. Hence, when you have free resources available why would anyone opt for the paid? This is why Matlab lags a bit back 9in comparison with other languages.

4) Support From Community

Coming to the popularity standards, Octave is not that prominent in the public. The other programming languages are highly popular in the market and have massive community support. Also, the adaptability rate of the all these three is quite high as compared to octave for machine learning.

5) Production Ready

When it is about the statistical analysis, R is the suitable programming language. If it is about the computer vision related task, Octave and Matlab are the preferred choices for the programming language. If it is about general tasks like data processing and result processing, Python is a more suitable programming language. Well, coming to picking one for Machine learning programming, Python will be more suitable. The generic nature will make integration of machine learning easy with other software.

6) DNN Framework Support











Learning curve










Community support




Production ready





DNN framework










Caffe and Tensorflow are the two most popular frameworks in the current time. Caffe has support for Matlab and Python while Tensorflow has support for Python and R. Now, for the lesser popular frameworks like Theano, Python is the single language that has the support. Python is the only language which has a universal support for every framework. The result is clear, Python has an upper hand over every other language.

Conclusion :

Well, it is quite evident from the above statistical representation which programming language is the best. The highest score gaining language is Python and probably the one you should use for Machine learning. But, again it depends on the type of work and the reason why have you thought about it in the first place. If you are thinking to develop something for the long term, prefer python and if you are looking for developing just a prototype for short-term, R is the right way.

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Comment by Jeff Norton on July 30, 2018 at 10:09am

The question is probably a bit ambiguous.  The article makes some great points, but the missing part of the question is what is the target platform or audience?  Both R and Python can (and do) wrap on top of C and gain a lot of performance boost from doing so.  However, with the amount of data increasing so much, it seems that horizontal scaling needs to be a serious consideration.  So why wasn't Scala on the list?  There is a reason why Spark is written in Scala and why Scala is foundational to big data analytics stacks like the Smack Stack.  The downside to Scala is that you just don't have as many ready libraries as R or Python, but using technologies like JEP may lead to the ability to leverage existing Python and R libraries in the more robust concurrent environment offered by Scala.

Comment by Paul Bremner on July 29, 2018 at 2:35pm

“Hence, when you have free resources available why would anyone opt for the paid?”

FYI, here are some reasons an individual, or company, might opt for a paid solution (using SAS as an example.)


  • As a programming language it is easier and faster to learn, and to use (just compare the simplicity of SAS syntax—or SQL syntax—with R or Python.)


  • As a programming language it is not limited to memory (i.e. it can handle “big data.”)


  • As a programming language it is inherently capable of utilizing multiple processors on a single machine, or in massively parallel processing mode (i.e. on multiple computers/servers, each with multiple processors) from the get-go.   


  • Probably the most important reason to consider a paid option: it is far more productive (and faster) to use a drag-and-drop interface (SAS Enterprise Miner) than using programming statements for machine learning when doing a large project that might require dozens/hundreds of models and multiple tasks.  Moreover, programming can be used as an integral part of the workflow when using drag-and-drop (that is, you just drag a “code node” into the workspace, insert it into the workflow, connect the nodes and presto, you’re using programming—SAS, SQL, R, Python, etc—if, on some rare occasion, you find it more useful to use programming statements for a given task.)


  • I haven’t gotten into the newest drag-and-drop interface: SAS Visual Analytics/Visual Statistics. But this takes drag-and-drop to a whole new level (and retains programming capabilities.)  Think of a Tableau-like interface (and graphical capability) applied to building machine learning models.  And if SAS doesn’t float your boat, use one of the cloud-based drag-and-drop approaches at Amazon, Google, Microsoft, etc.  Which, by the way, probably employ a “paid approach” at some level because….well….you need to get the job done.  You can certainly write thousands of lines of code if no-cost is an absolute requirement.  But if speed and productivity is what you’re looking for, drag-and-drop with programming capability is the way to do it.

Comment by Vincent Granville on July 28, 2018 at 3:22pm

Not sure what makes you think R is faster than Python. For speed, it is hard to beat the old C language. Or fast algorithms in any language, for that matter.


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