Explore the world of image compression with K-Means Clustering through this 45-minute guided project. Dive into pre-processing high-resolution image data, conducting exploratory data analysis, and leveraging the Jupyter widgets library to build interactive GUI components. Gain hands-on experience with Mini-Batch K-Means and learn to compress images efficiently. With instant access to a pre-configured cloud desktop containing all the necessary tools, you can focus on learning without worrying about setup.
By the end of the course, you will be equipped to explain the steps involved in k-means clustering, apply k-means clustering with scikit-learn to compress images, and create interactive GUI components in Jupyter notebooks using Jupyter widgets. This project is designed for learners looking to enhance their skills in image processing and unsupervised learning.
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