From image segmentation to detection of anomalies Mean Shift Clustering provides an incredibly powerful and flexible solution to a variety of data analysis issues. This isn't a standard algorithm, it's a highly non-parametric and dynamic technique that is able to navigate complicated data terrains, identifying dense peaks, which lead to clusters with a variety of dimensions and shapes, and much more. In this project that is guided you will be taught how to recognize intricate patterns, clusters, and subgroups within your data and then use it to aid in image segmentation. In this guiding Project we will look at Mean Shift Clustering, which is a non-parametric centroid-based clustering algorithm. Mean Shift Clustering attempts to cluster data without needing to be trained using the data labeled. In contrast to K-Means Clustering when we use the Mean Shift, we do not have to define how many clusters prior to. Mean Shift Clustering can be used in a variety of applications, including image segmentation and academic ranking systems, search engines, medicine and many more. In the initial part of this project that is guided we will concentrate on image segmentation, that is utilized in a variety of tracking and object detection systems because it helps identify the contours of every object. In the second section we will demonstrate how to utilize the Mean Shift Clustering technique to identify the survival rates of the Titanic the most well-known shipwreck in the history of ships. Based on the passenger's characteristics (e.g. age, ticket class, fare, etc.) We will group passengers into groups with different odds of survival. This project has been intended for researchers, data scientists, machine learning practitioners, and those who want to learn about non-parametric clustering methods. Participants must have a basic knowledge of Python programming basics. There is no prior knowledge of Mean Shift Clustering will be necessary to attend, since we will discuss the essential theory and implementations.
