An interactive series where machine learning algorithms are built from scratch, explained with simple math, and visualized step by step. The posts cover topics like regression, gradient descent, regularization, neural networks, decision trees, clustering, and more. Each post starts with the intuition, then walks through the math and lets you experiment directly in the browser. You can move data points, tune parameters, and watch algorithms learn in real time. The goal is to make machine learning easier to understand from the ground up.

Part 1

Foundations of Supervised Learning

Linear Regression

The starting point. Learn how a line fits data and how gradient descent finds the best weights.

Linear Regression - Multivariate

More features, a plane instead of a line. See how each input contributes to the prediction.

Logistic Regression

Use the sigmoid to turn a score into a probability. Train a binary classifier and find your first decision boundary.

Logistic Regression - Multivariate

Two features, one boundary. See why cross-entropy works better than MSE for classification.

Optimization & Regularization

Gradient Descent Deep Dive

The engine behind every ML model. Race SGD, Momentum, and Adam on loss surfaces and see which one wins.

Polynomial Regression & Bias-Variance

Fit curves, not just lines. See what happens when a model is too simple or too complex.

Regularization: Ridge, Lasso & Elastic Net

Penalize large weights to stop overfitting. See why Lasso pushes coefficients to zero but Ridge only shrinks them.

Neural Networks from Scratch

The Perceptron & MLP

One neuron cannot solve XOR. Add a hidden layer and unlock nonlinear decision boundaries.

Backpropagation Visualized

The chain rule applied layer by layer. Watch gradients weaken as they travel back through the network.

Activation Functions

What sits between layers shapes what a network can learn. Compare sigmoid, ReLU, tanh, and GELU side by side.