Certificate in Clustering Analysis: Practical Applications
-- ViewingNowThe Certificate in Clustering Analysis: Practical Applications is a valuable course designed to equip learners with essential skills in data analysis and machine learning. This course focuses on clustering analysis, a crucial technique for identifying patterns and structures in complex datasets.
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โข Introduction to Clustering Analysis: Overview of clustering analysis, its importance, and applications.
โข Data Preprocessing: Data cleaning, normalization, and transformation techniques for clustering analysis.
โข Distance Measures and Linkage Criteria: Understanding different distance metrics and linkage criteria for hierarchical clustering.
โข Partitioning Methods: K-means, K-medoids, and other partitioning clustering techniques.
โข Hierarchical Clustering: Agglomerative and divisive methods, dendrogram construction, and interpretation.
โข Density-Based Clustering: DBSCAN, OPTICS, and other density-based clustering techniques.
โข Clustering Validation: Internal and external validation methods, silhouette analysis, and cluster stability assessment.
โข Practical Applications: Real-world use cases of clustering analysis, including customer segmentation, anomaly detection, and image recognition.
โข Software Tools and Libraries: Hands-on experience with Python libraries such as scikit-learn, TensorFlow, and KNIME.
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