This Unsupervised Learning Tutorial will introduce you to the nitty gritty of Machine Learning and How Unsupervised Algorithms Work.
Unsupervised learning is a type of machine learning algorithm that is used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data. This tutorial will cover each method along with the example for better understanding.

Unsupervised learning algorithms can perform more complex processing tasks than supervised learning systems. Additionally, subjecting a system to unsupervised learning is one way of testing AI. That is the reason some experts prefer Unsupervised learning over supervised learning. This tutorial will you to understand the difference between them and at the end of the video, you would be able to apply it practically as well.

This tutorial comprises of the following topics:

• 00:00:00 Introduction
• 01:00:04 Principle Component Analysis(PCA)
• 01:09:13 Demo on PCA
• 01:52:15 K-means Demo
• 02:27:43 Clustering Images with K-means
• 03:36:10 K-means with Text Documents

#machine-learning #developer