Cluster Analysis and Unsupervised Machine Learning in Python

Description

In this course, you will :

• Understand the regular K-Means algorithm

• Understand and enumerate the disadvantages of K-Means Clustering

• Understand the soft or fuzzy K-Means Clustering algorithm

• Implement Soft K-Means Clustering in Code

• Understand Hierarchical Clustering

• Explain algorithmically how Hierarchical Agglomerative Clustering works

• Apply Scipy's Hierarchical Clustering library to data

• Understand how to read a dendrogram

• Understand the different distance metrics used in clustering

• Understand the difference between single linkage, complete linkage, Ward linkage, and UPGMA

• Understand the Gaussian mixture model and how to use it for density estimation

• Write a GMM in Python code

• Explain when GMM is equivalent to K-Means Clustering

• Explain the expectation-maximization algorithm

• Understand how GMM overcomes some disadvantages of K-Means

• Understand the Singular Covariance problem and how to fix it