This course provides an introduction to regression, one of the core techniques in supervised machine learning. The course emphasizes best practices in regression model development, including data splitting (train/test), feature selection, and methods to avoid overfitting.
You’ll learn how to train models that predict continuous numerical outcomes and evaluate them using various error metrics such as MAE, MSE, and RMSE. You’ll explore linear regression in depth and gain hands-on experience implementing regularization techniques to enhance model performance. By comparing different models using appropriate metrics, you’ll develop the skills to select the most effective regression approach for your data.
By the end of the course, you will be able to, differentiate between classification and regression use cases, describe, implement, and interpret linear regression models, use error metrics to compare model performance, apply regularization to prevent overfitting and improve generalization, and implement Ridge, LASSO, and Elastic Net regressions in Python.
This course is designed for aspiring machine learning engineers and data scientists, looking to gain hands-on experience working with regression models and apply them to real-world contexts.
