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Snehan Kekre
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/id77yf/image
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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
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
Save this course
Save Image Compression with K-Means Clustering to your list so you can find it easily later:
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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
Show steps
Improve your learning experience by organizing and reviewing course notes, assignments, and other materials.
Show steps
-
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
Show steps
Refresh your knowledge of different image compression techniques and their applications to prepare for the course.
Browse courses on
Image Compression
Show steps
-
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
Show steps
Strengthen your foundational knowledge of linear algebra to enhance your understanding of K-Means clustering.
Browse courses on
Linear Algebra
Show steps
-
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
Expand to see all activities and additional details
Show all 11 activities
Gather Resources on Image Compression
Show steps
Expand your knowledge base by compiling a collection of resources on image compression techniques, including K-Means clustering.
Browse courses on
Image Compression
Show steps
-
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
Show steps
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
Show steps
-
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
Show steps
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
Show steps
-
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
Show steps
Solidify your grasp of K-Means clustering by solving practice problems that challenge your comprehension.
Browse courses on
K-Means Clustering
Show steps
-
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
Show steps
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
Show steps
-
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
Show steps
Build a practical application of the Mini-Batch K-Means algorithm to reinforce your understanding of image compression.
Show steps
-
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
Show steps
Reinforce your understanding of K-Means clustering by sharing your knowledge with others and facilitating group discussions.
Browse courses on
K-Means Clustering
Show steps
-
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
Show steps
Apply your skills to build a simple image compression application that utilizes K-Means clustering for image reduction.
Browse courses on
Image Compression
Show steps
-
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.
Organize Course Materials
Show steps
Improve your learning experience by organizing and reviewing course notes, assignments, and other materials.
Show steps
Show steps
Show steps
- 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
Show steps
Refresh your knowledge of different image compression techniques and their applications to prepare for the course.
Browse courses on
Image Compression
Show steps
Show steps
Show steps
- 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
Show steps
Strengthen your foundational knowledge of linear algebra to enhance your understanding of K-Means clustering.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- 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
Expand to see all activities and additional details
Show all 11 activities
Gather Resources on Image Compression
Show steps
Expand your knowledge base by compiling a collection of resources on image compression techniques, including K-Means clustering.
Browse courses on
Image Compression
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
Solidify your grasp of K-Means clustering by solving practice problems that challenge your comprehension.
Browse courses on
K-Means Clustering
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
Build a practical application of the Mini-Batch K-Means algorithm to reinforce your understanding of image compression.
Show steps
Show steps
Show steps
- 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
Show steps
Reinforce your understanding of K-Means clustering by sharing your knowledge with others and facilitating group discussions.
Browse courses on
K-Means Clustering
Show steps
Show steps
Show steps
- 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
Show steps
Apply your skills to build a simple image compression application that utilizes K-Means clustering for image reduction.
Browse courses on
Image Compression
Show steps
Show steps
Show steps
- 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
Learners who complete Image Compression with K-Means Clustering will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists analyze large amounts of data to uncover hidden patterns and trends. They use statistical and machine learning techniques to build predictive models and make data-driven decisions. This course provides a solid foundation in data analysis and machine learning, which are essential skills for Data Scientists. The course also covers topics such as data visualization and communication, which are important for presenting insights to stakeholders.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. They work closely with Data Scientists to identify business problems that can be solved with machine learning. This course provides a strong foundation in machine learning algorithms and techniques. It also covers topics such as model selection and evaluation, which are important for building robust and accurate machine learning models.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They use statistical and visualization techniques to present insights to stakeholders. This course provides a solid foundation in data analysis and visualization. It also covers topics such as data wrangling and data mining, which are important for working with large datasets.
Software Engineer
Software Engineer
Software Engineers design, develop, and test software applications. They work with other engineers and stakeholders to gather requirements and translate them into working code. This course provides a foundation in software development principles and practices. It also covers topics such as object-oriented programming and data structures, which are essential for building scalable and maintainable software applications.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to define the product vision and roadmap. This course provides a foundation in product management principles and practices. It also covers topics such as market research and user experience, which are important for developing successful products.
Business Analyst
Business Analyst
Business Analysts help organizations understand their business processes and identify opportunities for improvement. They use data analysis and modeling techniques to develop solutions to business problems. This course provides a foundation in business analysis principles and practices. It also covers topics such as process mapping and requirements gathering, which are important for understanding business processes and identifying opportunities for improvement.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and closing projects. They work with stakeholders to define project scope, timelines, and budgets. This course provides a foundation in project management principles and practices. It also covers topics such as risk management and stakeholder management, which are important for successful project execution.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. They work with other stakeholders to define target markets, develop marketing messages, and track campaign performance. This course provides a foundation in marketing principles and practices. It also covers topics such as market segmentation and campaign planning, which are important for developing successful marketing campaigns.
Sales Manager
Sales Manager
Sales Managers are responsible for leading and motivating sales teams. They develop sales strategies, set targets, and track performance. This course provides a foundation in sales management principles and practices. It also covers topics such as sales forecasting and customer relationship management, which are important for successful sales management.
Operations Manager
Operations Manager
Operations Managers are responsible for planning, executing, and controlling operations. They work with other stakeholders to define operating procedures, set performance targets, and track results. This course provides a foundation in operations management principles and practices. It also covers topics such as supply chain management and quality control, which are important for successful operations management.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations. They use financial models and valuation techniques to assess the risk and return of investment opportunities. This course provides a foundation in financial analysis principles and practices. It also covers topics such as financial statement analysis and portfolio management, which are important for successful financial analysis.
Human Resources Manager
Human Resources Manager
Human Resources Managers are responsible for the management of human resources within an organization. They work with other stakeholders to develop HR policies and procedures, recruit and hire employees, and manage employee performance. This course provides a foundation in human resources management principles and practices. It also covers topics such as employee relations and compensation and benefits, which are important for successful human resources management.
Customer Service Manager
Customer Service Manager
Customer Service Managers are responsible for the management of customer service operations. They work with other stakeholders to develop customer service policies and procedures, train and supervise customer service representatives, and track customer satisfaction. This course provides a foundation in customer service management principles and practices. It also covers topics such as customer relationship management and complaint handling, which are important for successful customer service management.
Administrative Assistant
Administrative Assistant
Administrative Assistants provide administrative and clerical support to other employees. They perform a variety of tasks, such as answering phones, scheduling appointments, and managing email. This course provides a foundation in administrative support principles and practices. It also covers topics such as office management and communication skills, which are important for successful administrative support.
Receptionist
Receptionist
Receptionists greet visitors, answer phones, and provide general administrative support. This course provides a foundation in receptionist principles and practices. It also covers topics such as customer service and office management, which are important for successful receptionists.
For more career information including salaries, visit:
OpenCourser.com/course/id77yf/image
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Image Compression with K-Means Clustering .
Save
Provides a comprehensive overview of machine learning techniques for computer vision tasks, including image classification, object detection, and image segmentation. It covers a wide range of topics, from basic concepts to advanced algorithms, and is written in a clear and accessible style.
Provides a hands-on introduction to machine learning with Python. It covers a wide range of topics, from basic concepts to advanced algorithms, and is written in a clear and accessible style.
Save
Provides a comprehensive overview of deep learning concepts and algorithms. It is written in a clear and accessible style, and is suitable for beginners with no prior knowledge of deep learning.
Provides a comprehensive overview of data science techniques with Python. It covers a wide range of topics, from basic concepts to advanced algorithms, and is written in a clear and accessible style.
Provides a gentle introduction to machine learning concepts and algorithms. It is written in a clear and accessible style, and is suitable for beginners with no prior knowledge of machine learning.
Provides a hands-on introduction to deep learning with Python. It covers a wide range of topics, from basic concepts to advanced algorithms, and is written in a clear and accessible style.
Save
Provides a comprehensive overview of pattern recognition and machine learning techniques. It covers a wide range of topics, from basic concepts to advanced algorithms, and is written in a clear and accessible style.
Save
Provides a comprehensive overview of data mining techniques, including clustering, classification, and association rule learning. It covers a wide range of topics, from basic concepts to advanced algorithms, and is written in a clear and accessible style.
Provides a hands-on introduction to machine learning with R. It covers a wide range of topics, from basic concepts to advanced algorithms, and is written in a clear and accessible style.
Save
Provides a comprehensive overview of computer vision algorithms and applications. It covers a wide range of topics, from basic concepts to advanced algorithms, and is written in a clear and accessible style.
Provides a comprehensive overview of digital image processing techniques. It covers a wide range of topics, from basic concepts to advanced algorithms, and is written in a clear and accessible style.
Provides a hands-on introduction to image processing with Python. It covers a wide range of topics, from basic concepts to advanced algorithms, and is written in a clear and accessible style.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/id77yf/image
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
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
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