Explore the K-Nearest Neighbors (kNN) algorithm in this 26-minute lecture from NPTEL-NOC IITM. Learn about the fundamentals of kNN, including its applications, underlying assumptions, and step-by-step implementation. Discover when to use kNN and gain insights into crucial aspects such as parameter selection, feature scaling, and the importance of feature selection. Through illustrations and practical examples, understand how kNN works during the testing phase and grasp key considerations for effective implementation.