K-Means Clustering

Summary

An unsupervised algorithm that partitions data into K clusters by minimizing distance to cluster centroids — the default first tool for finding groups in unlabeled data.

How to Learn This

1Implement K-Means on a 2D dataset and visualize the clusters forming over iterations.
2Learn the elbow method for choosing K.
3Understand K-Means' limitations: it assumes spherical, similarly-sized clusters.

K-Nearest Neighbors

Support Vector Machines

More topics in Machine Learning Fundamentals

Supervised vs Unsupervised Learning Linear Regression Logistic Regression Decision Trees Random Forests K-Nearest Neighbors Support Vector Machines Naive Bayes scikit-learn Basics Train/Test Split Cross-Validation

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