Unsupervised Machine Learning: Cluster Analysis Algorithms
Cluster Analysis: core concepts, working, evaluation of KMeans, Meanshift, DBSCAN, OPTICS, Hierarchical clustering
4.12 (13 reviews)
98
students
5.5 hours
content
Aug 2020
last update
$39.99
regular price
What you will learn
Understand the KMeans Algorithm and implement it from scratch
Learn about various cluster evaluation metrics and techniques
Learn how to evaluate KMeans algorithm and choose its parameter
Learn about the limitations of original KMeans algorithm and learn variations of KMeans that solve these limitations
Understand the DBSCAN algorithm and implement it from scratch
Learn about evaluation, tuning of parameters and application of DBSCAN
Learn about the OPTICS algorithm and implement it from scratch
Learn about the cluster ordering and cluster extraction in OPTICS algorithm
Learn about evaluation, parameter tuning and application of OPTICS algorithm
Learn about the Meanshift algorithm and implement it from scratch
Learn about evaluation, parameter tuning and application of Meanshift algorithm
Learn about Hierarchical Agglomerative clustering
Learn about the single linkage, complete linkage, average linkage and Ward linkage in Hierarchical Clustering
Learn about the performance and limitations of each Linkage Criteria
Learn about applying all the clustering algorithms on flat and non-flat datasets
Learn how to do image segmentation using all clustering algorithms
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3165572
udemy ID
5/25/2020
course created date
11/22/2020
course indexed date
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