Beginner’s Guide To K-Means Clustering 

A very common example of an unsupervised machine learning, clustering is the process of grouping similar data points into a cluster. Given a finite set of data points, clustering aims to find homogeneous subgroups of data points with similar characteristics. In this article, we will learn the basics of a simple clustering algorithm called K-Means and we will also learn to implement it with the popular Scikit-learn library.  Before we begin, we introduce you to MachineHack’s practice section where beginners can learn to code a variety of problems without any overwhelming or intimidating Math or theories. MachineHack practise is a new feature of MachinHack that lets you get started with ML problems. Clustering vs Classification Clustering may sound similar to the popular classification type of problems, but unlike classification wherein a labelled set of classes are provided at the time of training, the idea of clustering is to form the classes or categories from the d