K-Nearest Neighbors

Summary

A simple algorithm that predicts based on the majority class (or average value) of the K closest data points — intuitive, but sensitive to feature scaling and slow on large datasets.

How to Learn This

1Fit a KNN model and experiment with different values of K.
2Learn why feature scaling (Phase 5) is essential before using KNN.
3Understand why KNN struggles with high-dimensional data (the 'curse of dimensionality').

Gradient Boosting (XGBoost, LightGBM)

Support Vector Machines

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)Support Vector Machines Naive Bayes

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