K-Means for Cluster Analysis and Unsupervised Learning in R

Learn why and where K–Means is a powerful tool

Clustering is a very important part of machine learning. Especially unsupervised machine learning is a rising topic in the whole field of artificial intelligence. If we want to learn about cluster analysis, there is no better method to start with, than the k–means algorithm.

Unlike other courses, it offers NOT ONLY the guided demonstrations of the R–scripts but also covers theoretical background that will allow you to FULLY UNDERSTAND & APPLY UNSUPERVISED MACHINE LEARNING  (K–means) in R.

Get a good intuition of the algorithm

The K–Means algorithm is explained in detail. We will first cover the principle mechanics without any mathematical formulas, just by visually observing data points and clustering behavior. After that, the mathematical background of the method is explained in detail.

Learn how to implement the algorithm in R

First, we will learn how to implement K–Means from scratch. This is important to get a really good grip on the functioning of the algorithm.

You will of course also learn how to implement the algorithm really quickly by using only one line of code as well as we will learn different types of K–Means algorithms and how to visualize the results of K–means.