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. 

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.