Why R?

It's been interesting to watch the competition between Python and R over the past few years to be the numero uno language for machine learning. In my opinion, you should learn both. I use Python for NLP and R for machine learning, although sometimes I do a little NLP in R and a little machine learning in Python. It's basically a Toyota v. Honda debate. They're both great.  The latest (2017) survey from Kaggle shows where we are:
  • Python is the most used tool but statisticians prefer R for their ML work. 
  • Titles vary by country for these professionals, but the most common is Data Scientist.
  • The majority of survey participants have a Master's or PhD degree.
  • The top four algorithms were: logistic regression, decision trees, random forests, and neural networks. 
Why do I teach machine learning with R?

The main thing I like about R for beginning machine learning aficionados is that it gets out of the way. The syntax is straightforward enough that you can focus on the machine learning instead of the R syntax. With R, everything is in one environment and you can seamlessly switch from data visualization to machine learning to statistics on your results to more visualization. The beautiful free IDE RStudio is another plus for R. With recent addition to R like the tidyverse, Keras, and so forth, there are no limits to what you can do efficiently in R.

So get started!

Link to download R: https://www.r-project.org/
Link to download RStudio: https://www.rstudio.com/

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