Knowledge Base

Lessons

Deep-dive articles on machine learning models, implemented from scratch. Each guide walks through the math, code, and intuition step by step.

to track progress and take notes.

The foundation of predictive modeling. Learn how to fit the perfect line.

Binary decisions through probability. The sigmoid function demystified.

From single trees to powerful forests. Ensemble learning at its finest.

Proximity-based learning. You are the average of your neighbors.

Maximum margin learning. Finding the optimal hyperplane.

Probabilistic classification using Bayes' theorem. Naive yet powerful.

K-Means, DBSCAN, GMM. Discovering hidden structure in unlabeled data.

PCA, LDA, t-SNE, UMAP. Compressing high-dimensional worlds.

Sequential ensemble learning. Building strong learners from weak ones.

The architecture of intelligence. Backpropagation and beyond.