Regression Algorithms for Machine Learning

Predicting numerical values—like housing prices, sales trends, or customer lifetime value—is one of the most critical skills in data science. Understanding the theory and implementation behind regression is your first major step to mastering machine learning. This text-based course guides you from foundational statistics to confidently building and tuning regression models. You will learn how to prepare your data, select the right algorithms, and evaluate your model's performance using modern Python workflows. What you'll learn: Understand foundational concepts of linear, polynomial, ridge, and lasso regression; Implement regression algorithms using modern Python libraries and dataframes; Apply feature engineering and preprocessing techniques to clean and prepare raw data; Evaluate model performance using metrics like MSE, RMSE, and R-squared; Prevent overfitting and underfitting using regularization techniques. The course begins with core terminology and basic statistical definitions before guiding you through step-by-step written implementations. You will practice through structured text-based exercises designed to reinforce your coding and analytical skills. This course is designed for beginners, aspiring data scientists, and developers looking to enter the field of machine learning with no prior ML experience required. Start reading today to build a strong foundation in predictive modeling.