Machine learning can feel overwhelming because it’s often taught as a collection of formulas, libraries, and tricks. This course takes a different approach. Instead of treating models as black boxes, we focus on understanding how supervised machine learning actually works – step by step, from first principles.
In this course, you’ll learn how models learn from labeled data, how predictions are evaluated, and why models fail in predictable ways. We start with the simplest supervised model, linear regression, and use it to build a clear mental model of the learning process: data goes in, predictions come out, errors are measured, and the model adjusts. From there, we move naturally into classification with logistic regression, decision thresholds, and evaluation metrics like precision and recall.
You’ll then explore alternative learning strategies, including similarity-based learning with k-nearest neighbors and rule-based learning with decision trees. Finally, you’ll see how ensemble methods like random forests improve reliability by combining multiple models.
