/courses/machine-learning/regression/Recent content in Regression on DATATWEETSHugoenCopyright (c) 2025 DatatweetsFri, 14 Nov 2025 09:00:00 +0200/courses/machine-learning/regression/lesson-1-introduction-to-linear-regression/Fri, 14 Nov 2025 09:00:00 +0200/courses/machine-learning/regression/lesson-1-introduction-to-linear-regression/Discover what linear regression is, why it minimizes squared error, and how to fit one in scikit-learn. You will load the real UCI Automobiles dataset, build simple and multiple regressions to predict car price, and learn how gradient descent finds the best-fit line./courses/machine-learning/regression/lesson-2-interpreting-regression-parameters/Fri, 14 Nov 2025 09:00:00 +0200/courses/machine-learning/regression/lesson-2-interpreting-regression-parameters/Build simple and multiple linear regression models on the real automobiles dataset, then learn to interpret the intercept and every coefficient. You will see how standardizing features lets you compare predictors fairly and read which ones move price the most./courses/machine-learning/regression/lesson-3-checking-linear-regression-fit/Fri, 14 Nov 2025 09:00:00 +0200/courses/machine-learning/regression/lesson-3-checking-linear-regression-fit/A model that fits the line is not automatically a good model. In this lesson you learn to read residual plots, compute RMSE and MAE, and interpret R-squared so you can judge honestly whether linear regression suits your data./courses/machine-learning/regression/lesson-4-applying-linear-regression-models/Fri, 14 Nov 2025 09:00:00 +0200/courses/machine-learning/regression/lesson-4-applying-linear-regression-models/Bring the whole regression workflow together on the real automobiles dataset. You will fit simple and multiple regressions, scale features the right way, read standardized coefficients, and judge a model honestly with test R-squared, RMSE, and MAE./courses/machine-learning/regression/lesson-5-understanding-gradient-descent/Fri, 14 Nov 2025 09:00:00 +0200/courses/machine-learning/regression/lesson-5-understanding-gradient-descent/Learn the intuition and math behind gradient descent, the optimization algorithm that trains linear regression. You will see why a model needs a cost function, how the gradient points the way downhill, and how the learning rate controls each step, all grounded in the real automobiles dataset./courses/machine-learning/regression/lesson-6-implementing-gradient-descent-in-python/Fri, 14 Nov 2025 09:00:00 +0200/courses/machine-learning/regression/lesson-6-implementing-gradient-descent-in-python/Open the black box behind linear regression. You will code the mean squared error loss, derive its gradients, and write a gradient descent loop from scratch that learns a price model on the real automobiles dataset. Along the way you will see how the learning rate controls convergence./courses/machine-learning/regression/lesson-7-gradient-descent-with-scikit-learn/Fri, 14 Nov 2025 09:00:00 +0200/courses/machine-learning/regression/lesson-7-gradient-descent-with-scikit-learn/You hand-coded gradient descent in the last lesson. Now you will learn why plain gradient descent struggles as data grows, meet stochastic gradient descent as the fix, and use scikit-learn’s SGDRegressor to predict car prices, confirming it matches ordinary least squares./courses/machine-learning/regression/lesson-8-guided-project-predicting-insurance-costs/Fri, 14 Nov 2025 09:00:00 +0200/courses/machine-learning/regression/lesson-8-guided-project-predicting-insurance-costs/Put every regression skill from this module to work on a real project. You will load the medical insurance dataset, explore what drives cost, one-hot encode the categorical features, train a linear model, and evaluate it honestly on a held-out test set.