y = wx + b
σ(z) = 1/(1+e-z)
J(θ) = MSE
Machine Learning
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MASTER GUIDES

Study Lab Guides

Comprehensive, interactive guides that take you from zero to mastery. Built with visualizations, math, and hands-on practice.

AVAILABLE 11 Sections 45 min read Beginner to Advanced

Logistic Regression

Complete Master Guide

From historical intuition to mathematical mastery. A beginner-friendly, visually interactive deep dive with 3D visualizations, animated sigmoid curves, and step-by-step gradient descent.

Sigmoid Function Binary Cross-Entropy Gradient Descent Decision Boundaries Regularization Interactive Playground
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AVAILABLE 9 Sections 40 min read Beginner to Advanced

Linear Regression

Complete Master Guide

Understanding the foundation of all regression models. From OLS to gradient descent, with interactive visualizations and real-world applications.

Ordinary Least Squares Cost Functions Normal Equation Polynomial Regression Regularization Interactive Playground
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AVAILABLE 10 Sections 50 min read Beginner to Advanced

Neural Networks

From Perceptron to Deep Learning

Build neural networks from scratch. Understand forward propagation, backpropagation, activation functions, and train your first deep learning model.

Perceptron Backpropagation Activation Functions Deep Learning Adam Optimizer Interactive Lab
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AVAILABLE 10 Sections 50 min read Beginner to Advanced

Decision Trees & Random Forests

Tree-Based Methods Guide

Master decision trees, random forests, and gradient boosting. Learn information gain, entropy, pruning, and ensemble methods with interactive tree builders.

Information Gain Entropy & Gini Random Forests XGBoost Pruning Feature Importance
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