Skill

Unsupervised Machine Learning

Data Science, Analytics and AI/ML

Unsupervised machine learning encompasses algorithms that learn patterns from unlabeled data by finding inherent structure, such as clusters or reduced-dimensional representations, rather than predicting a known target variable. It is commonly applied to customer segmentation, anomaly detection, recommendation systems, and exploratory analysis in fields ranging from marketing to bioinformatics. Practitioners use techniques like k-means clustering, hierarchical clustering, and principal component analysis to implement it.

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• Customer Segmentation
• Anomaly Detection
• Principal Component Analysis