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CodeSignal

Mastering Clustering in Machine Learning

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Overview

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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.

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.

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