Clustering methods in Machine Learning includes both theory and python code of each algorithm. Algorithms include K Mean, K Mode, Hierarchical, DB Scan and Gaussian Mixture Model GMM. Interview questions on clustering are also added in the end.
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clustering-in-python's Introduction
Welcome to Clustering (Theory & Code)
01 Unsupervised Learning (Theory)
What is Unsupervised Learning & Goals of Unsupervised Learning
Type of Unsupervised Learning: 1.Clustering, 2.Association Rule & 3.Dimensionality Reduction
02 Clustering (Theory)
Definition and Application of Clustering
4 methods: 1.K Means 2.Hierarchical 3.DBScan & 4.Gaussian Mixture
03 Euclidean & Manhattan Distance (Theory)
Two points are near to each other, chances they are similar
Distance Measure between two points
Euclidean Distance: Under-root of Square distance between two points
Manhattan Distance: Absolute Distance between points
04 K-Means Clustering (Theory)
How Algorithim works (Step Wise Calculation)
Pre-processing required for K Means
Determining optimal number of K: 1.Profiling Approach & 2.Elbow Method
05 Elbow Method (Theory)
Working of Elbow Method with Example
3 concepts: 1.Total Error, 2.Variance/Total Squared Error & 3.Within Cluster Sum of Square (WCSS)
06 K Means Clustering (Python Code)
Define number of clusters, take centroids and measure distance
Euclidean Distance : Measure distance between points
Number of Clusters defined by Elbow Method
Elbow Method : WCSS vs Number of Cluster
Silhouette Score : Goodness of Clustering
07 Hierarchical Clustering (Theory)
Two Approaches: 1.Agglomerative(Botton-Up) & 2.Divisive(Top-Down)
Types of Linkages:
Single Linkage - Nearest Neighbour (Minimal intercluster dissimilarity)