Cluster Analysis and Unsupervised Machine Learning in Python

• 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

Who this course is for:

• Students and professionals interested in machine learning and data science
• People who want an introduction to unsupervised machine learning and cluster analysis
• People who want to know how to write their own clustering code
• Professionals interested in data mining big data sets to look for patterns automatically