Summary Unsupervised Learning: Exploring Patterns and Structure in Data
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Course
Machine Learning
Institution
Machine Learning
Unlock the power of unsupervised learning with this in-depth course designed to guide you through the techniques used to uncover hidden patterns and structures in data. Unlike supervised learning, which relies on labeled data, unsupervised learning focuses on exploring data without predefined label...
Unsupervised Learning: Clustering and Collaborative Filtering
Clustering
Clustering is a type of unsupervised learning where the goal is to group
similar data points together based on certain features or attributes. There
are several algorithms used for clustering, including:
K-means clustering
Hierarchical clustering
Density-based spatial clustering of applications with noise
(DBSCAN)
K-means Clustering
K-means clustering is a simple and widely used clustering algorithm. It
aims to partition the data into K distinct, non-overlapping clusters where
each data point belongs to the cluster with the nearest mean. The steps
involved in K-means clustering are:
. Initialize K centroids randomly.
. Assign each data point to the nearest centroid.
. Calculate new centroids based on the mean of the assigned data
points.
. Repeat steps 2-3 until convergence (i.e., when the centroids no
longer change significantly).
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