Python and R are the most important language for Machine Learning. You need to learn any of them perfectly. In this post, I’ll tell you what to choose based on your experience and interest.

Most of the people say that:

 If you have some programming experience, Python might be the language for you.Python’s syntax is more similar to other languages than R’s syntax is. However, if the goal is to push past the basics of machine learning and data analysis, Python is probably a better choice.
Well, they are more or less correct, because Python provides more library than R, and Generally R is used more for Statistical Work and we all knew that Python is more flexible than R.

Machine Learning Packages

Python’s Packages

  1.  PyBrain is a modular machine learning library that offers powerful algorithms for machine learning tasks. The algorithms are intuitive and flexible, but the library also has a variety of environments to test and compare your machine learning algorithms.
  2. Scikit-learn is the most popular machine learning library for Python. Built on NumPy and SciPy, Scikit-learn offers tools for data mining and analysis that bolster Python’s already-superlative machine learning usability.

R’s Packages

  1. R, like Python, has plenty of packages to boost its performance. When it comes to approaching parity with Python in machine learning, Nnet improves R by supplying the ability to easily model neural networks.
  2. Caret is another package that powers R’s machine learning capabilities, in this case by offering a set of functions that increase the efficiency of predictive model creation.

Best Courses on Machine Learning with Python  –>


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Best Courses on Machine Learning with R  –>


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Most Popular Language for Machine Learning – 

Yellow is Python and Blue is R


Now, What to Choose?

The main issue with R is its consistency. Algorithms are provided by third parties, which makes them comparatively inconsistent. The resulting decrease in development speed comes from having to learn new ways to model data and make predictions with each new algorithm you use. Every package requires a new understanding. Inconsistency is true of the documentation as well, as R’s documentation is almost always incomplete.

However, if you find yourself in an academic setting and need a tool for data analysis, it’s hard to argue with choosing R for the task. For professional use, Python makes more sense. Python is widely used throughout the industry and, while R is becoming more popular, Python is the language more likely to enable easy collaboration. Python’s reach makes it easy to recommend not only as a general purpose and machine learning language but with its substantial R-like packages, as a data analysis tool, as well.

If you don’t already know R, learn Python and use RPy2 to access R’s functionality. You’ll be getting the power of two languages in one, and Python is production-ready because most companies have production systems ready for Python. This isn’t true for R. Once you learn RPy2, the jump to pure R isn’t very daunting, but moving in the opposite direction is considerably more difficult.


Both Python and R have great packages to maintain some kind of parity with the other, regardless of the problem you’re trying to solve. There are so many distributions, modules, IDEs, and algorithms for each that you really can’t go wrong with either. But if you’re looking for a flexible, extensible, multi-purpose programming language that also excels in machine learning, Python is a clear choice. Though i am learning Data Science with R, If you are looking for Machine Learning, Python will be the best choice. If you don’t know Python too much, don’t worry, you can learn it for FREE, ClICK HERE to LEARN Python for FREE.