Logistic Regression: Introduction to Classification
Learn logistic regression — the fundamental classification algorithm. Understand how it predicts probabilities, the sigmoid function, decision boundaries, and full Python implementation.
Polynomial Regression: When Linear Isn’t Enough
Learn polynomial regression — how to model curved relationships by adding polynomial features. Includes degree selection, overfitting risks, and full Python implementation.
Multiple Linear Regression: Handling Multiple Features
Master multiple linear regression — predicting outcomes from many features. Learn the math, assumptions, feature selection, and full Python implementation.
Understanding the Cost Function in Linear Regression
Learn what the cost function is in linear regression, why MSE is used, how it shapes learning, and how gradient descent minimizes it with clear examples.
Implementing Linear Regression from Scratch in Python
Learn to implement linear regression from scratch in Python using NumPy. Build gradient descent, the normal equation, and full evaluation—no scikit-learn required.
