Regression Models and Measuring Model Accuracy

Building reliable predictive models requires knowing how to train algorithms and accurately assess their performance. This course provides a clear foundation in statistical and machine learning regression methods, guiding you through core concepts, model validation, and error analysis. You will gain the practical skills needed to set up baseline models, diagnose prediction errors, and fine-tune your workflow for consistent, high-accuracy results. What you'll learn: - Understand foundational regression terminology, mathematical concepts, and core algorithms - Build and configure simple, multiple, and regularized regression models - Measure predictive accuracy using key evaluation metrics such as MAE, MSE, RMSE, and R-squared - Apply cross-validation techniques to detect and avoid overfitting and underfitting - Prepare data effectively with standard feature scaling and preprocessing best practices - Practice evaluating model errors and tuning parameters to maximize accuracy The course begins with fundamental terminology and statistical intuition before moving step-by-step into implementation, diagnostic metrics, and practical evaluation strategies. This course is designed for beginners in data analytics and machine learning with no prior modeling experience required. Start reading today to master the fundamentals of predictive regression and model evaluation.