This video is all about K-Means Algorithm in Machine Learning. This video will revolve around What is Clustering and What is K-Mean Clustering. It will also cover measurement metrics for KNN. We will also explore how the K-Mean Algorithm works. And will end up with the Demo for K-Means Implementation with Scikit Learn.
So, before jumping to the video directly, you should know what basically K-Means clustering is.

What is K-Means Clustering?
A cluster is a collection of data items that have been aggregated together due to particular commonalities. K-means clustering is a basic and widely used unsupervised machine learning technique. Unsupervised algorithms often make inferences from datasets using just input vectors and without reference to known, or labeled, outcomes.
K is the number of pre-defined clusters that must be produced during the procedure; for example, if K=2, there will be two clusters; if K=3, there will be three clusters, and so on.

Following Topics are covered in this session:
00:00 - What is Clustering?
02:29 - What is K-Means Clustering?
05:11 - Distance Measurement Metrics
07:26 - How K-Means Algorithm Works?
11:03 - K-Means Implementation


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