People are a little bit confused that which programming language they should learn and they follow. The answer is also difficult that which one should be ranked one so that everyone can focus on the languages. Here are programming languages used for machine learning according to my personal experience as a programmer.
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, the following four common languages come to the mind:
A comparative study of the languages 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 canā??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:
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 universal support for every framework. The result is clear, Python has an upper hand over every other language.
Well, it's quite evident from the on top of applied mathematics illustration that programming language is that the best. the best score gaining language is Python and doubtless the one you ought to use for Machine learning. But, once more it depends on the kind of work and also the reason why have you ever thought of it within the initial place. If you're thinking to develop one thing for the future, like python and if you're trying to find developing simply a paradigm for short, R is that the right method.