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Cluster Analysis and Unsupervised Machine Learning in Python

Cluster Analysis and Unsupervised Machine Learning in Python

What you’ll learn

  • 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
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