CENG-303/503- Wk-5 - Classification and Clustering

This session provides a high-level overview of Classification and Clustering, two fundamental approaches used in Data Science, Data Analytics, and Machine Learning. Classification focuses on predicting predefined categories or class labels, while clustering is used to discover natural groupings and hidden patterns within unlabeled data. The video highlights key concepts such as supervised learning, unsupervised learning, training data, class labels, feature selection, decision boundaries, cluster similarity, and model interpretation. It also introduces common classification methods such as logistic regression, decision trees, k-nearest neighbors, and support vector machines, alongside clustering techniques such as K-means and hierarchical clustering. The session also explores how classification and clustering are applied in areas such as customer segmentation, fraud detection, medical diagnosis, recommendation systems, image recognition, and predictive analytics. The focus is on what these methods involve, why they are important, and when they are used—without presenting a coding tutorial, step-by-step demonstration, or hands-on lab. #DataScience #DataAnalytics #Classification #Clustering #MachineLearning #SupervisedLearning #UnsupervisedLearning #KMeans #LogisticRegression #DecisionTrees #CustomerSegmentation #PredictiveAnalytics #ArtificialIntelligence