Data Science


K-Means for Cluster Analysis and Unsupervised Learning in R

The powerful K-Means Clustering Algorithm for Cluster Analysis and Unsupervised Machine Learning in R

5.00 (4 reviews)



3 hours


Dec 2020

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What you will learn

Understand unsupervised learning and clustering using R-programming language

It covers both theoretical background of K-means clustering analysis as well as practical examples in R and R-Studio

Fully understand the basics of Machine Learning, Cluster Analysis & Unsupervised Machine Learning

How the K-Means algorithm is defined mathematically and how it is derived.

How to implement K-Means very fast with R coding: examples of real data will be provided

How the K-Means algorithm works in general. Get an intuitive explanation with graphics that are easy to understand

Different types of K-meas; Fuzzy K-means, Weighted K-means and visualization of K-Means results in R

Evaluate Model Performance & Learn The Best Practices For Evaluating Machine Learning Model Accuracy

Implementing the K-Means algorithm in R from scratch. Get a really profound understanding of the working principle

Learn R-programming from scratch: R crash course is included that you could start R-programming for machine learning


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.

The examples will be based on real data that you could get a real feeling of the data science tasks.

Learn where you should pay attention

K-Means is a powerful tool but it definitely has its drawbacks! You will learn where you have to be careful and when you should use the algorithm, and also when it is a bad idea to use the algorithm. We will learn how to perform the model's evaluation for K-Means in R.


You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.

This course is different from other training resources. Each lecture seeks to enhance your data science and clustering skills (K-means, weighted-K means, Heat mapping, etc) in a demonstrable and easy-to-follow manner and provide you with practically implementable solutions. You’ll be able to start analyzing different streams of data for your projects and gain appreciation from your future employers with your improved machine learning skills and knowledge of the cutting edge data science methods.

The course is ideal for professionals who need to use cluster analysis, unsupervised machine learning, and R in their field.

One important part of the course is the practical exercises. You will be given some precise instructions and datasets to run Machine Learning algorithms using the R and R tools.



K-Means for Cluster Analysis and Unsupervised Learning in R
K-Means for Cluster Analysis and Unsupervised Learning in R
K-Means for Cluster Analysis and Unsupervised Learning in R
K-Means for Cluster Analysis and Unsupervised Learning in R




What is Machine Leraning and it's main types?

Software used in this course

What is R and RStudio?

How to install R and RStudio in 2020

Lab: Get started with R in RStudio

R Crash Course - get started with R-programming in R-Studio

Lab: Installing Packages and Package Management in R

Lab: Variables in R and assigning Variables in R

Overview of data types and data structures in R

Lab: data types and data structures in R

Vectors' operations in R

Data types and data structures: Factors

Dataframes: overview

Functions in R - overview

Lab: For Loops in R

Read Data into R

Unsupervised learning: K-Means in R: Theory & Practise

Overview of Machine Leraning in R

Unsupervised Learning & Clustering: theory

K-Means Clustering: Theory

Example K-Means Clustering in R: Lab

K-means clustering: Application to email marketing

Heatmaps to visualize K-Means Results in R: Examplery Lab

Advanced K-Means Clustering Analysis

Starting with Fuzzy K-means in R

Entropy Weighted K-Means in R

Selecting the number of clusters for unsupervised Clustering methods (K-Means)

Performance Evaluation of Unsupervised Learning CLustering Algorithms in R

How to assess a Clustering Tendency of the dataset

Assessing the performance of unsupervised learning (clustering) algorithms

How to compare the performance of different unsupervised clustering algoritms?

Your Independent Project in K-Means CLuster Analysis

Introduction to Your Project based on a case study

Project Assignment




Olga27 November 2020

yes definitely it was a good match for me! I definitely gained new knowledge and adding some more expertise to my resume. I loved the detailed introduction in such a way that even a newbie will understand it.

Светлана26 November 2020

The instructor explained the concept very well. Overall a great course if you want to learn and implement stuff in R


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