Development
Data Science
Cluster Analysis
K-Means for Cluster Analysis and Unsupervised Learning
3.9 (23 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,443 students enrolled

K-Means for Cluster Analysis and Unsupervised Learning

The powerful K-Means Clustering Algorithm for Cluster Analysis and Unsupervised Machine Learning in Python
3.9 (23 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,443 students enrolled
Created by Hannes Hinrichs
Last updated 5/2019
English
English [Auto-generated]
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K-Means for Cluster Analysis and Unsupervised Learning
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This course includes
  • 1 hour on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • The basic fundamentals of Unsupervised Learning: Cluster Analysis and Pattern Recognition
  • How the K-Means algorithm works in general. Get an intuitive explanation with graphics that are easy to understand
  • How the K-Means algorithm is defined mathematically and how it is derived.
  • Implementing the K-Means algorithm in Python from scratch. Get a really profound understanding of the working principle
  • How to implement K-Means very fast with one line of code
Course content
Expand all 8 lectures 01:10:59
+ – The Mechanics of K-Means
3 lectures 19:41

Here, the K-Means algorithm is explained visually with an example dataset

Visual Explanation
06:33

This lecture covers the mathematical background of K-Means. It is explained in detail and easy to understand.

Mathematical Explanation
06:38

This part covers the drawbacks of K-Means. Here you will learn where you have to pay attention to, before using the K-Means algorithm on your data!

Drawbacks
06:30
+ – Application: Implementation
3 lectures 45:09

In this part, I will guide you to have Python with all the neccessary packages installed on Windows 10.

Setup of Python, Numpy and Scikit-Learn via Anaconda
Preview 06:17

In this video, we start from scratch implementing the K-Means algorithm. We will use Python and Numpy. This is important because you will get an understanding of the working principle in a numeric environment.

Implementation from scratch using Python and Numpy
34:04

In this video, we will be using some snippets of the code from the previous lecture. The difference is, that we only have one line of code for the actual K-Means routine.

Implementation in one line of code using Scikit-Learn
04:48
+ – Final words
1 lecture 00:28

Please review this course :)

Final words
00:28
Requirements
  • Basic mathematical skills
Description

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.


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 Python

First we will learn how to implement K-Means from scratch. That means for the beginning no additional packages will be used, except numpy. 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.

The examples will be based on artificial data, which we generate ourselves in the course.


Learn where you should pay attention

K-Means is a powerful tool but it definetely has 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. I will show you examples and counterexamples on the quality and applicability of this method.

Who this course is for:
  • Beginner Python developers curious about data science
  • Anyone interested in Machine Learning
  • People who want to get a good start into unsupervised learning
  • People who want to cluster their data fast