scikit-learn: Image Compression with K-Means Clustering

Welcome to this project-based course Image Compression with K-Means Clustering. In this project, you will apply the k-means clustering unsupervised learning algorithm using scikit-learn and Python to build an image compression application with interactive controls.

By the end of this 45-minute long project, you will be competent in: 1. Pre-processeing high-resolution image data for k-means clustering, 2. Conducting basic exploratory data analysis (EDA) and data visualization, 3. Applying a computational time-efficient implementation of the k-means algorithm, Mini-Batch K-Means, to compress images and 4. Leverage the IPython widgets library to build interactive GUI components to select images from a drop-down list and pick values of k using a slider.

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scikit-learn: Image Compression with K-Means Clustering

Duration (mins)

Learners

NA / 5

Rating

Task List


We will cover the following tasks in 43 minutes:


Introduction and Overview


Data Preprocessing


Visualizing the Color Space using Point Clouds


Visualizing the K-means Reduced Color Space


Creating Interactive Controls with Jupyter Widgets


K-means Image Compression with Interactive Controls

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Preview the instructions that you will follow along in a hands-on session in your browser.

Snehan Kekre

About the Host (Snehan Kekre)


Snehan hosts Machine Learning and Data Sciences projects at Rhyme. He is in his senior year of university at the Minerva Schools at KGI, studying Computer Science and Artificial Intelligence. When not applying computational and quantitative methods to identify the structures shaping the world around him, he can sometimes be seen trekking in the mountains of Nepal.



Frequently Asked Questions


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