The last few years have witnessed a remarkable expansion of research in Machine Learning (ML) domain. The field has gained unprecedented popularity, with several new areas newly developed, and previously established areas gaining new momentum. With Machine Learning creating a buzz in the research domain, 3 out 7 researchers are opting to pursue their PhD in this ‘hot’ field.
With that said, today, several tools are easily available in the market to conduct a study in this field. While some have been proved to be the most efficient tool and are leading in the race, others are trying hard to stay in the race of being a reliable tool.
As per the poll results of Kaggle, the number of users using Python is about 61%, followed by R language, i.e. 57% in 2018. The results also present good use of SAS and MATLAB with much lower R representation.
Although Octave or MATLAB is a good programming language option for matrix operations as well as while working with well-defined feature matrix, nothing can beat Python, especially when used along with Pandas and NLTK when working with not so well-defined feature matrix.
Here’s a look at a few factors that have kept Python ahead of the game.
- Integration feature – Python can effortlessly integrate the Enterprise Application Integration, which makes it easier to develop Web services by invoking CORBA or COM components. Python has powerful control abilities as it directly calls through C, C++ or Jython or Java. Also, as it can run on all modern operating systems via same byte code, it can process XML and other markup languages.
- Enhanced productivity – Due to its unit testing framework ability, strong process integration features, and enhanced control capabilities, Python can work with increased speed for most applications thereby improving the productivity. This tool is also considered as a great option for developing scalable multi-protocol network applications.
- Extensive support libraries – This tool offers large standard libraries, including areas such as the Internet, string operations, operating system interfaces, web service tools, and protocols. Majority of the highly used programming tasks that limit the length of the codes to be included in Python are scripted into it.
- Easy debugging – The time required to write and debug code in python is way less when compared to other languages. This enables the researcher to invest sufficient time on heuristics pertaining to ML and the algorithms rather than wasting time on debugging the code for syntax errors.
It is evident that Python is the best choice as it generic enough to be used in Machine Learning related tasks and also has better support for all DNN frameworks such as Caffe, Tensorflow, etc.
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