Machine Learning

  Clustering and  Principal Component Analysis Unsupervised Learning  Concepts: Clustering Algorithms (K-Means, Hierarchical Clustering) Principal Component Analysis (PCA) Anomaly Detection Model Evaluation and Selection Model Performance Metrics Cross-Validation Techniques Hyperparameter Tuning Model Selection Techniques What is Unsupervised Learning? Unsupervised learning is a type of machine learning where the algorithm is trained on unlabeled data to identify hidden patterns or structures.  Unsupervised learning is a machine learning technique where the goal is to discover patterns or relationships in data without any labelled information. The data is unlabeled, and the algorithm must find structure within the data on its own. Clustering is a common unsupervised learning technique used to group similar data points together.