Support Vector Machines

Summary

A model that finds the best boundary (hyperplane) separating classes with the widest possible margin — powerful for certain problems, especially with the 'kernel trick' for non-linear boundaries.

How to Learn This

1Fit an SVM on a simple 2D dataset and visualize the decision boundary.
2Learn the intuition behind the 'kernel trick' without needing the full math.
3Understand why SVMs can be slow to train on very large datasets.

K-Nearest Neighbors

Naive Bayes

More topics in Machine Learning Fundamentals

Supervised vs Unsupervised vs Reinforcement Learning Train / Test / Validation Split Overfitting & Underfitting Bias-Variance Tradeoff Cross-Validation Linear Regression Logistic Regression Decision Trees Random Forests Gradient Boosting (XGBoost, LightGBM)K-Nearest Neighbors Naive Bayes

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