Explore unsupervised learning in this path focused on Clustering. Start with data preprocessing, learn algorithms like K-means, DBSCAN, and Hierarchical Clustering, and master validation techniques to evaluate model performance from scratch.
Overview
Syllabus
- K-means Clustering Decoded
- Unlock the secrets of K-means clustering, the backbone of unsupervised learning. You will group data into clusters, identify cluster centroids, and refine cluster quality.
- Hierarchical Clustering Deep Dive
- Unpack the complexity of hierarchical clustering, learning to construct and interpret dendrograms for valuable data insights, and apply your knowledge to real-world data.
- Density-Based Clustering Simplified
- Explore the nuanced world of density-based clustering. Learn to navigate through DBSCAN, focusing on connectivity and density functions to identify unique cluster shapes.
- Cluster Performance Unveiled
- Explore an in-depth analysis of clustering model validation, delving into techniques that evaluate, refine, and optimize the performance of clustering algorithms. We'll discuss the Silhouette Score, Davis-Bouldin Index, and Cross-Tabulation Analysis, learning how to implement these practices to identify optimal clustering structures.
Courses
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Unlock the secrets of K-means clustering, the backbone of unsupervised learning. You will group data into clusters, identify cluster centroids, and refine cluster quality.
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Unpack the complexity of hierarchical clustering, learning to construct and interpret dendrograms for valuable data insights, and apply your knowledge to real-world data.
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Explore the nuanced world of density-based clustering. Learn to navigate through DBSCAN, focusing on connectivity and density functions to identify unique cluster shapes.
-
Explore an in-depth analysis of clustering model validation, delving into techniques that evaluate, refine, and optimize the performance of clustering algorithms. We'll discuss the Silhouette Score, Davis-Bouldin Index, and Cross-Tabulation Analysis, learning how to implement these practices to identify optimal clustering structures.
