Neighborhood Approaches and DBSCAN

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level
Intermediate
Subject
Machine Learning
Course Length
37 minutes
Subscription
Data Academy

Course Description

This course teaches you how to implement DBSCAN from scratch, describes the various DBSCAN attributes, and helps you to evaluate the impact of neighborhood size. This course will help you identify the best suited algorithm from K-Means, hierarchical clustering, and DBSCAN to solve your problem. This course is part of the Unsupervised Learning path – complete this course to learn how to implement the DBSCAN algorithm.

Requirements

  • Previous experience of using Python is required. Prior knowledge of clustering including k-means and hierarchical clustering is necessary.