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Offered By: IBMSkillsNetwork

Image Segmentation with Mean Shift Clustering

From image segmentation to anomaly detection, Mean Shift Clustering offers a versatile and powerful solution for a wide range of data analysis challenges. It is no ordinary algorithm - it's a dynamic and non-parametric technique that can navigate through complex data terrains, finding density peaks that lead to clusters of diverse shapes and sizes and more. In this guided project, you will learn how to identify complex patterns, clusters, and subgroups in your datasets and use it for image segmentation.

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Guided Project

Data Science

886 Enrolled
4.5
(110 Reviews)

At a Glance

From image segmentation to anomaly detection, Mean Shift Clustering offers a versatile and powerful solution for a wide range of data analysis challenges. It is no ordinary algorithm - it's a dynamic and non-parametric technique that can navigate through complex data terrains, finding density peaks that lead to clusters of diverse shapes and sizes and more. In this guided project, you will learn how to identify complex patterns, clusters, and subgroups in your datasets and use it for image segmentation.

In this guided Project, we will explore Mean Shift Clustering, which is a **non-parametric centroid-based clustering** algorithm. Mean Shift Clustering attempts to group data without having first to be trained on the labeled data. Unlike the K-Means Clustering, when using the Mean Shift, we don't need to specify the number of clusters beforehand. Mean Shift Clustering is used in a wide variety of applications, such as image segmentation, academic ranking systems, search engines, medicine, and many others. 

In the first part of this guided project, we will focus on the image segmentation, which is used in many object detection and tracking systems, as it makes it easier to detect the contour of each object. In the second part, we will show how to use the Mean Shift Clustering to classify the survivors rates of the Titanic, the most famous shipwreck in history. Based on the passengers' features (e.g. age, ticket class, fare, etc.) we will classify the passengers into clusters with different survival probabilities. 


Who should participate?

This guided project is designed for data scientists, machine learning practitioners, and enthusiasts eager to explore non-parametric clustering techniques. Participants should have a basic understanding of Python programming fundamentals. No prior experience with Mean Shift Clustering is required, as we will cover the necessary theory and practical implementations.


Estimated Effort

30 Minutes

Level

Beginner

Skills You Will Learn

Clustering, Data Science, Python Programming

Language

English

Course Code

GPXX04YGEN

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