Machine Learning can feel abstract when you only watch code run without understanding what’s happening underneath. This course takes a different approach: every major algorithm is taught from first principles and then reinforced with a fully solved numerical problem, so you don’t just learn what an algorithm does — you learn how to work through it yourself, step by step.
You’ll start with the foundational ideas every ML practitioner needs — types of machine learning, overfitting and underfitting, and the bias-variance tradeoff — before moving into Linear and Polynomial Regression, where you’ll solve problems with one, two, and three variables by hand.
From there, you’ll cover Ridge and Lasso Regression, understanding exactly how regularization prevents overfitting through their cost functions and penalty terms, again with solved examples at every step. You’ll then move into classification with Logistic Regression, learn how to evaluate model performance using the Confusion Matrix, Accuracy, Precision, Recall, and F1-Score, and work through K-Nearest Neighbors and Support Vector Machines — including kernel functions like Linear, Polynomial, RBF, and Sigmoid.
