Linear Regression in Scikit-learn focuses on building and evaluating predictive models using one of the most fundamental supervised learning algorithms. Linear regression is used to model the relationship between independent variables and a continuous target variable. This training explains how to implement linear regression using the Scikit-learn library in Python. It also covers concepts such as model training, coefficient interpretation, cost function, and prediction evaluation. You will learn how to prepare data, split datasets, and assess model performance using metrics like MSE and R² score. The course also highlights best practices for building accurate and interpretable regression models in machine learning workflows.
Showing the single result
