KNN Classification In Machine Learning | K-Nearest Neighbor Algorithm Explained | Simplilearn
About this course
This course on K-Nearest Neighbors (KNN) delves into one of the most widely used algorithms in data science, exploring its application for classifying data based on the closest neighbor. You'll learn how to implement KNN with Python using real datasets, particularly the Iris dataset, and understand the importance of choosing the right K value and distance function. The course will also cover data preprocessing, scaling, and model evaluation techniques to ensure accurate results.
What you should already know
Basic knowledge of Python programming and data handling is recommended before taking this course.
What you will learn
By the end of this course, learners will be able to confidently apply KNN for classification tasks, evaluate model performance, and optimize the K value for enhanced accuracy.