How To Get Started With Machine Learning Algorithms in R

Last Updated on August 22, 2019

R is the most popular platform for applied machine learning. When you want to get serious with applied machine learning you will find your way into R.

It is very powerful because so many machine learning algorithms are provided. A problem is that the algorithms are all provided by third parties, which makes their usage very inconsistent. This slows you down, a lot, because you have to learn how to model data and how to make predicts with each algorithm in each package, again and again.

In this post, you will discover how you can overcome this difficulty with machine learning algorithms in R, with pre-prepared recipes that follow a consistent structure.

Kick-start your project with my new book Machine Learning Mastery With R, including step-by-step tutorials and the R source code files for all examples.

Let’s get started.

Lots of Algorithms, Little Consistency

The R ecosystem is enormous. Open source third party packages provide this power, allowing academics and professionals to get the most powerful algorithms available into the hands of us practitioners.

A problem that I experienced when
To finish reading, please visit source site