Image Compression with K-Means Clustering from Coursera

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.

What's inside

Syllabus

scikit-learn: Image Compression with K-Means Clustering

Welcome to this project-based course on 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 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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Useful for determining which colors to focus on in an image

Uses interactive GUI components to select images and values

Teaches data visualization techniques

Develops skills in unsupervised machine learning

Relevant to the field of image processing and computer vision

Provides hands-on experience in implementing the k-means clustering algorithm

Reviews summary

Image compression via k-means clustering

Learners say that Image Compression with K-Means Clustering is a beginner-friendly course that provides hands-on experiences through its various mini-projects. Learners especially value the guided project which further enhances their understanding of the course's content. Others have mentioned that they found the course to be well-paced, informative, and useful. However, some learners have encountered keyboard issues on their remote desktop and limitations with the cloud browser's usage time.

Multiple projects provide opportunities for learners to apply their knowledge.

"good project-based course"

"The project done is really good for beginners."

"Professor was very good in his approach to this guided project"

Course content is well-suited for learners with no prior knowledge.

"Perfect and just right"

"very good and simple to learn"

"The course was very interactive and suitable for beginners"

Highly-regarded guided project helps learners grasp the concepts.

"Great guided project course"

"Great hands on experience !!!"

"Great Course.Now i know we can compress image using Kmeans.Thankyou Snehan Kekre"

Some learners experienced keyboard issues when using a remote desktop.

"issues with keyboard typing on remote desktop(Capslock didnt worked )"

Learners have expressed concerns about the limited usage time of the cloud browser.

"Its a very good course for beginners...Only Regret the time for usage of cloud browser is very limited"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Image Compression with K-Means Clustering with these activities:

Organize Course Materials

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Improve your learning experience by organizing and reviewing course notes, assignments, and other materials.

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Create a dedicated folder or notebook for course materials.
Regularly review and summarize key concepts from lectures, readings, and assignments.
Identify any areas where you need further clarification or practice.

Review basic image compression techniques

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Refresh your knowledge of different image compression techniques and their applications to prepare for the course.

Browse courses on Image Compression

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Revisit the concept of image compression and its importance.
Review the different types of image compression techniques such as lossless and lossy compression.

Review Linear Algebra Basics

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Strengthen your foundational knowledge of linear algebra to enhance your understanding of K-Means clustering.

Browse courses on Linear Algebra

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Identify online courses or textbooks that provide a concise review of linear algebra basics.
Go through the materials to refresh your understanding of concepts like vectors, matrices, and distance metrics.
Practice solving simple linear algebra problems to reinforce your knowledge.

Eight other activities

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Show all 11 activities

Gather Resources on Image Compression

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Expand your knowledge base by compiling a collection of resources on image compression techniques, including K-Means clustering.

Browse courses on Image Compression

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Identify and gather articles, tutorials, and documentation related to image compression.
Organize the resources into a structured format, such as a digital folder or annotated bibliography.
Summarize the key concepts and techniques presented in each resource.

Practice implementing K-Means clustering algorithm with Python

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Practice implementing the K-Means clustering algorithm using Python to enhance your understanding of the algorithm and its application in image compression.

Browse courses on K-Means Clustering

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Implement the K-Means clustering algorithm from scratch in Python.
Apply the K-Means clustering algorithm to a sample dataset.
Evaluate the performance of the K-Means clustering algorithm using metrics such as the silhouette score.

Explore K-Means Clustering with Scikit-Learn

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Enhance your understanding of K-Means clustering by following guided tutorials that delve into its implementation using Scikit-Learn.

Browse courses on K-Means Clustering

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Identify online tutorials or documentation that provide step-by-step instructions on using Scikit-Learn for K-Means clustering.
Follow the tutorials to create a simple K-Means clustering application.
Experiment with different parameters and observe how they affect the clustering results.
Apply the learned concepts to a small dataset to practice your skills.

Solve K-Means Clustering Practice Problems

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Solidify your grasp of K-Means clustering by solving practice problems that challenge your comprehension.

Browse courses on K-Means Clustering

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Find online platforms or resources that offer practice problems on K-Means clustering.
Attempt to solve the problems using the concepts covered in the course.
Compare your solutions with provided answers or consult online forums for guidance.
Repeat the process until you consistently solve problems accurately.

Complete practice data analysis and data visualization

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Practice using real-world data and apply the skills to perform basic EDA and data visualization to enhance your understanding of image compression and k-means clustering.

Browse courses on Data Analysis

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Access the course materials and complete the practice worksheets.
Implement the Mini-Batch K-Means algorithm in Python using the scikit-learn library.
Apply EDA techniques to explore and visualize high-resolution image data.

Implement a Mini-Batch K-Means Algorithm

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Build a practical application of the Mini-Batch K-Means algorithm to reinforce your understanding of image compression.

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Choose an image and import it into your Python environment.
Preprocess the image data by resizing and converting it to a suitable format for clustering.
Apply the Mini-Batch K-Means algorithm to cluster the image pixels into a specified number of clusters.
Generate a compressed image using the cluster centroids and assign each pixel to its nearest centroid.
Evaluate the compression ratio and visual quality of the compressed image.

Lead Study Groups for K-Means Clustering

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Reinforce your understanding of K-Means clustering by sharing your knowledge with others and facilitating group discussions.

Browse courses on K-Means Clustering

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Identify or create a study group focused on K-Means clustering.
Prepare discussion topics and materials.
Facilitate discussions, answer questions, and guide participants through the concepts.
Provide feedback and support to group members.

Design a Simple Image Compression App

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Apply your skills to build a simple image compression application that utilizes K-Means clustering for image reduction.

Browse courses on Image Compression

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Plan the design and functionality of your image compression app.
Implement the K-Means clustering algorithm and incorporate it into your app's compression process.
Create a user interface that allows users to select images, specify compression parameters, and view results.
Test and refine your app to ensure it performs as expected.

Career center

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