scikit-learn
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
clustering
Discover patterns in unlabeled data using clustering, dimensionality reduction, and anomaly detection
pattern-learning
Enables autonomous pattern recognition, storage, and retrieval at project level with self-learning capabilities for continuous improvement
Clustering Analysis
Identify groups and patterns in data using k-means, hierarchical clustering, and DBSCAN for cluster discovery, customer segmentation, and unsupervised learning
