This project was completed as a part of the Honors portion of the Unsupervised Learning, Recommenders, Reinforcement Learning Course on Coursera.
Credit to DeepLearning.AI, Stanford, and the Coursera platform for providing the course materials and guidance.
This project focuses on the implementation of the K-means algorithm, applied to image compression. Through this exercise, I will begin by working with a sample dataset, which will facilitate a better understanding of the inner workings of the K-means algorithm. Following this, I will utilize the K-means algorithm for image compression, aiming to reduce the number of colors present in an image to only those that are most prevalent in that specific image. By the end of this report, we will have successfully explored the application of the K-means algorithm in image compression, gaining valuable insights into its effectiveness in reducing the color palette while preserving the image's visual integrity.
