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Unsupervised Learning and Clustering

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Overview

Navigate through the intricacies of Unsupervised Learning and Clustering in this hands-on course. Skip the high-level libraries and build core aspects of unsupervised learning methods from scratch, including k-Means, mini-batch k-Means, Principal Component Analysis, and DBSCAN. Learn to assess cluster quality with crucial clustering metrics like homogeneity, completeness, and v-metric.

Syllabus

Unit 1: Unsupervised Learning with Clustering

Customer Segmentation Using k-Means Clustering
Exploring Three-Cluster K-Means Algorithm
Updating Centroid of a Data Cluster
Updating Centroids in k-Means Algorithm

Unit 2: Mini Batch K Means

Mini-Batch K-Means Clustering Visualization Task
Experimenting with Mini-Batch K-Means Batch Size Adjustment
Mini-Batch K-Means Centroid Update Task
Random Batch Selection for Mini-Batch K-Means Algorithm

Unit 3: Principal Component Analysis

PCA Dimensionality Reduction and Visualization Task
Enhancing PCA Implementation for 2D Visualization
Principal Component Analysis Eigenvalue Computation

Unit 4: DBSCAN Clustering in C++

Planetary Clustering with DBSCAN
Fine-Tuning DBSCAN Eps Parameter
DBSCAN Clustering Logic Implementation
Mapping Points to Clusters in DBSCAN Algorithm

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