Cluster Analysis and Unsupervised Machine Learning in Python

Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering, and KDE.

What Will I Learn from the course "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

Includes:

• 2.5 hours on-demand video
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

---

Take this course