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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: Understanding Clustering with k-Means Algorithm Basics

Visualize Clustering with k-Means Algorithm
Exploring Space with More Clusters
Calculating the New Center in Clustering
Implementing the k-Means Centroid Update

Unit 2: Enhancing Machine Learning Expertise: Mini-Batch K-Means Clustering Explained

Visualizing Mini-Batch K-Means Clustering
Adjusting Batch Size in Mini-Batch K-Means
Updating the Mini-Batch K-Means Centroids
Update Cluster Centers in Mini-Batch K-Means

Unit 3: A Practical Introduction to Principal Component Analysis (PCA)

Visualizing Dimension Reduction with PCA
Expanding the Horizon with Two Principal Components
Unveiling the Secrets of PCA: Eigendecomposition and Transformation

Unit 4: Mastering DBSCAN: From Basics to Implementation

DBSCAN Clustering Visualization
Adjusting DBSCAN Epsilon Value
Navigating Through the Stars: Adding DBSCAN Logic
Mapping and Queueing in DBSCAN Clustering

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