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Python or R: What Should You Use For Your Machine Learning Project?
In recent years, the interest as well as the hype around machine learning has escalated.
Everyone wants to jump on the bandwagon of machine learning and leverage the diverse range of opportunities and careers this field promises. One question that arises against this backdrop relates to the choice of programming language. Online and offline, developers and programmers are debating on the pros and cons of each programming language’s compatibility with machine learning concepts. Two languages that have come at the top of this discussion are Python and R. This blog will discuss the role of both these languages in Machine learning.
Is There A Thing Such As Best Language For Machine Learning?
There are a lot of data available that suggests that popularity as a yardstick is not a good measure for choosing a programing language for machine learning. Instead users should consider other factors such as what they are going to build, their learning background and their primary purpose for getting involved in machine learning before they select a programming language. Most developers port the language that they were already using into machine learning, particularly when they are using it in projects alongside their previous project. However, for users whose foray into programming is through machine learning, popular opinion suggests that it is better to use Python which is easier to use.
Packages in Python & R
Python has several packages that make it ideal for machine learning purposes. PyBrain is modular machine learning library that provides powerful algorithms for machine learning purposes. The algorithm is flexible and intuits. The library also has different environments where the user can do comparison and testing of their machine learning algorithms. Scikit-learn is a library built on SciPy and NumPy and is the most popular python library for machine learning. NumPy and SciPy which are the core data analysis libraries are brought together in scikit-learn with a low entry barrier.
Like Python, R also possesses a lot of packages which enhances its use in machine learning. Nnet is one such library in machine learning that offers R the potential to model neural networks in an easy way. Yet another package that improves R’s machine learning capacity is Caret. With the functions enabled by Caret, a user can improve their efficiency of their predictive model development.
When To Choose Python or R for Machine Learning
Choose Python For Machine Learning….
When you are a beginner
Python is syntactically easier to start with as compared to other programming language though it has its own nuances. Through the years python’s data science and scientific stack has undergone fast transformation. There is a whole range of libraries in python fit for machine learning and data science. Many of these libraries are written in a lower-level language that makes use of a interface/wrapper that enables python to use it. All this would enable a beginner to concentrate more on machine learning concepts and not having to deal with other insignificant concerns.
When you want to develop/build Machine Learning products
In an engineering environment, python certainly integrates much better when compared to R. Though a user has to employ lower level languages like C, C++ or Java while writing an efficient code, offering it a python wrapper is a good step for enabling better integration with other elements.
Choose R For Machine Learning….
When want to explore the mathematics of ML
R is the best programming language for a user who is interested in the mathematical side of ML like statistical computing , statistics and the like. Its popularity in math is proven by the fact that most statisticians all over employ R. In fact, it is widely said that R is built by statisticians for statisticians. Its syntax might be daunting for some and even though R possesses a range of powerful ML packages, it is a bit scattered. However, it has a great package like Caret which tries to bring together several machine learning algorithms and related operations in a single interface.
When you want to do data visualization and analytics
R is the better choice amongst other programming languages including Python when it comes to rapid prototyping and using datasets for developing ML models.
Why Python is an ALL- Rounder!
If an individual does not know either of these languages, it is better to learn python. By learning python they can access the power of both the languages using python’s interface RPy2 which enables the user to access R’s functionality.
Python is also the best choice when it comes to professional choice. It is production ready: most enterprises has production systems for python, used widely in a diverse range of industries and also allows easy collaboration.