Offered By: IBMSkillsNetwork
Unraveling Patterns with DBSCAN
Classification and Clustering are one of the most common tasks in data science. If you ever need to classify a customer or perform anomaly detection, or even image segmentation, then DBSCAN is the right tool to use. In this project you will have a chance to learn what DBSCAN is and see how it's applied to some real world problems.
Continue readingGuided Project
Data Science
167 Enrolled4.7
At a Glance
Classification and Clustering are one of the most common tasks in data science. If you ever need to classify a customer or perform anomaly detection, or even image segmentation, then DBSCAN is the right tool to use. In this project you will have a chance to learn what DBSCAN is and see how it's applied to some real world problems.
We will explore the concept of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and learn about how it can be used to identify patterns and clusters in various types of data. DBSCAN is an unsupervised machine learning algorithm known for its ability to discover clusters of arbitrary shapes and sizes, making it a valuable tool in a wide range of applications, from anomaly detection to customer segmentation.
Throughout this project, we will cover the fundamental concepts behind DBSCAN and walk you through the step-by-step implementation process. By the end of the project, you will gain a comprehensive understanding of how DBSCAN works and how to apply it to real-world datasets.
Throughout this project, we will cover the fundamental concepts behind DBSCAN and walk you through the step-by-step implementation process. By the end of the project, you will gain a comprehensive understanding of how DBSCAN works and how to apply it to real-world datasets.
Who should participate?
This guided project is suitable for data enthusiasts, machine learning practitioners, and anyone interested in uncovering patterns and clusters in data. Participants should have a basic understanding of Python programming concepts. No prior experience with DBSCAN is required, as we will cover the necessary theoretical foundations before diving into the practical implementations.