Explain the steps involved in k-means clustering
Apply k-means clustering with scikit-learn to compress images
Create interactive, GUI components in Jupyter notebooks using Jupyter widgets
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 pre-processing high-resolution image data for k-means clustering, conducting basic exploratory data analysis (EDA) and data visualization, applying a computationally time-efficient implementation of the k-means algorithm, Mini-Batch K-Means, to compress images, and leverage the Jupyter widgets library to build interactive GUI components to select images from a drop-down list and pick values of k using a slider. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Introduction and Overview
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
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
Good course to gain a little understanding of K means algorithm(mostly its mini form MiniBatchKMeans) and also to know its application such as image compression.
The course was very interactive and suitable for beginners. The concepts are explained well and are easy to understand and implement yourself.
really informative and interactive, rhyme is an ingeniously made learning platform. had a great time learning a new skill.
Great Course. Now i know we can compress image using Kmeans.\n\nThankyou Snehan Kekre
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At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.
Can I complete this Guided Project right through my web browser, instead of installing special software?
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You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.
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