Ml Linear Regression — Free Machine Learning Tutorial
Learn Ml Linear Regression in Machine Learning with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Ml Linear Regression in Machine Learning with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
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Ml Linear Regression in Machine Learning
Linear regression predicts a continuous output (like price or temperature) by fitting a line (or hyperplane in multiple dimensions) through data. The model learns weights (coefficients) for each feature such that the line best fits the training data. For one feature, the equation is y = w*x + b, where w is slope, b is intercept. For multiple features, y = w₁*x₁ + w₂*x₂ + ... + b. The loss function is Mean Squared Error (MSE): average of squared differences between predicted and actual values.
Training uses gradient descent: iteratively adjust weights to reduce MSE. The algorithm computes the gradient (slope of the loss function) and updates weights in the opposite direction. Learning rate controls step size—too small is slow, too large overshoots. After training, we evaluate using R² score (0-1, higher is better) and RMSE (root mean squared error in original units).
Linear regression works well for relationships that are actually linear. If the data has a curve or nonlinear trend, polynomial regression (adding squared or cubic features) helps. Regularization (L1 ridge, L2 lasso) prevents overfitting by penalizing large weights. Always scale features before training—unscaled features can have very different learning rates.
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