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Machine Learning: Algorithms & Applications
·
Module 10
Unsupervised Learning: Clustering
1
K-Means Clustering: Algorithm and Mechanics
Explain K-Means clustering, including centroid initialization, iterative assignment/update steps, and convergence criteria.
2
Optimal K: Elbow Method & Silhouette Score
Choose the optimal number of clusters (K) for K-Means using the elbow method and silhouette score.
3
K-Means Clustering: Implementation and Visualization
Apply K-Means to a dataset and visualize the resulting clusters.
4
Hierarchical Clustering and Dendrogram Interpretation
Explain hierarchical agglomerative clustering and interpret a dendrogram to determine the number of clusters.
5
Hierarchical Clustering and Dendrogram Visualization
Apply hierarchical clustering to a dataset and visualize the results using a dendrogram.
DBSCAN: Density-Based Clustering Explained
Explain the density-based approach of DBSCAN, its 'eps' and 'minPts' parameters, and its ability to find non-spherical clusters and noise.
7
DBSCAN Clustering: Implementation and Visualization
Apply DBSCAN to a dataset and visualize the resulting clusters and identified noise points.
8
Clustering Algorithm Comparison & Selection
Compare the results of K-Means, hierarchical clustering, and DBSCAN on a single dataset, identifying which to use based on data characteristics.
Previous module
Support Vector Machines for Classification
Next module
Unsupervised Learning: Dimensionality Reduction