Image Compression with K-Means Clustering

Overview

Embark on a hands-on journey to master image compression with our dynamic project, "Image Compression with K-Means Clustering," available exclusively on Coursera's immersive project platform, Rhyme. Over the course of 45 minutes, gain practical expertise in utilizing the k-means clustering unsupervised learning algorithm with scikit-learn and Python. This project is meticulously designed to equip you with necessary skills for pre-processing high-resolution image data, engaging in exploratory data analysis (EDA), implementing Mini-Batch K-Means for efficient image compression, and creating interactive graphical user interfaces with Jupyter widgets. Elevate your learning experience by accessing a pre-configured cloud desktop directly through your browser, complete with Python, Jupyter, and scikit-learn, ensuring you focus solely on acquiring new skills without the hassle of setup. This project allows five accesses to the cloud desktop, although instructional videos are available for unlimited viewing, optimizing your learning convenience. Please note, learners in North America will currently receive the optimal experience, with efforts underway to expand this across other regions. This course is categorized under Artificial Intelligence Courses, Python Courses, scikit-learn Courses, and Data Visualization Courses, making it a perfect fit for those eager to enhance their technical proficiency.

Syllabus

Taught by

Snehan Kekre

Tags