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Learn how polynomial regression models nonlinear relationships by transforming the input variable into polynomial features. Understand how it trains using the same gradient descent as linear regression, why higher-degree polynomials risk overfitting, and how cross‑validation helps choose the right degree.
Learn how linear regression updates its coefficients β₀ and β₁ using the derivatives of the MSE and gradient descent. A clear, intuitive explanation of the update formulas that allow a model to adjust its line step by step until it reaches the global minimum.
Understand Gradient Descent, the algorithm that allows machine learning models to reduce error step by step. Learn how gradients work, how parameters are updated, why the learning rate is crucial, and why this method powers everything from regression to deep learning and transformers.
Learn how Linear Regression measures its error using the Mean Squared Error (MSE), why errors are squared, what it means to minimize a cost function, and how this connects to gradient descent. A clear and intuitive explanation of the core idea behind supervised learning
A clear and intuitive explanation of Linear Regression: what problem it solves, how slope and intercept work, why errors are squared, and how the model finds the best line. The foundational concept behind classic Machine Learning, neural networks and gradient descent.
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