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Python and ML proficiency
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Module 12
Unsupervised Learning and Dimensionality Reduction
1
PCA for Dimensionality Reduction
Perform dimensionality reduction using PCA
2
t-SNE for Dimensionality Reduction
Visualize high-dimensional data in 2D using t-SNE
K-means Clustering with scikit-learn
Implement K-means clustering using scikit-learn
4
Understanding K-Means Results
Interpret K-means results including centroids and inertia
5
Hierarchical Clustering and Dendrogram Analysis
Apply hierarchical clustering and analyze dendrograms
6
DBSCAN for Density-Based Clustering
Use DBSCAN for density-based clustering
7
Evaluating Clustering with Silhouette Scores
Evaluate clustering quality using silhouette scores
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