K-mean, hyper-parameter tuning, strengths and weaknesses

This is an archived version of the course. Please see the latest version of the course.

Module 6

Unsupervised Learning

Introduction to Module 6
General concept of Clustering
The K-means algorithm
K-mean, hyper-parameter tuning, strengths and weaknesses
Probability density estimation
PDE - Non-parametric approaches
PDE - Parametric approaches
GMM-EM
GMM-EM - Hyper-parameter tuning
Quiz - Unsupervised Learning

Now that we are familiar with the K-means algorithm, we can see how one can select the number of clusters (K), and what are the strengths and main limitations of the algorithms. This is covered in this video:

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