Tuesday, January 23, 2018

Benefits of Using R for Machine Learning

If you do not already know, machine learning is the process of programming a computer in a way that it can improve its reactions to data. At the core of AI technology as we see it today is machine learning. With all the buzz about the rise of AI you can imagine that machine learning is undoubtedly one of the most wanted skills at the present time. 

The process of machine learning can roughly be divided in two phases.
       Model creation
       Prediction
The model building part is performed as a batch process and the prediction part is done realtime.
Using R in these phases does not make any huge difference. It is as good as Python in this aspect.

R used to be essentially a language for the academia. Now, with new updates and constant developments machine learning with R has gained popularity. Let us take a look at a few features of R which makes it a good choice for data science and ML.

The libraries
There are about 5000 libraries in R which can help you solve a lot of different problems. These libraries are reliable, regularly updated and versatile.
These libraries are one reason why data science professionals prefer R. A large part of the data science and analytics crowd is using R at the moment and there are a lot of opportunities for R professionals too.

Increased speed
Initially R had issues regarding processing speed. It was in no ways the fastest in the branch. Although through development and upgradation R has now become faster than ever. The computation intensive operations are re-written to ensure enhanced performance.
A large chunk of the companies which are moving forward towards AI integration are introducing machine learning with R.

Visualization
One should never miss the visualization part of R while mentioning its benefits. R is a self sufficient tool when it comes to data visualization and the analysis of visually represented data. The story telling aspect of any analytics venture needs the support of a visualization tool and R works fine as one.

You can use it on top of Hadoop
Handling big data would have been a problem if it wasn't for Hadoop infrastructure. R uses RAM while processing data. It is a constraint. But it can be used with Hadoop, using the distributed file system. Thus large bulks of data can be dealt with. This is a crucial feature that enables a machine learning expert to use R. Mining and processing data is a major part of training the machine. R, makes it work with the help of Hadoop clusters.

The rivalry between R and Python is a pretty famous one. But in order for you to be successful, it is better that you learn both. Not simply because it offers you with more solutions for a problem but because a combination of that sort gets you a larger salary.

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