Support Vector Machines

Summary

A classifier that finds the maximum-margin boundary between classes, extendable to non-linear boundaries via the kernel trick — strong on smaller, high-dimensional datasets.

How to Learn This

1Train an SVM with a linear kernel and visualize the decision boundary on 2D data.
2Experiment with the RBF kernel to see how it handles non-linear separation.
3Learn the role of the C and gamma hyperparameters.

K-Means Clustering

Naive Bayes

More topics in Machine Learning Fundamentals

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

Stuck on this topic? Ask an Insider

Get 1:1 guidance from people who've walked this exact path — free on the InsideEdge app.

Download