Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.
Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.
Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.
Covers regression analysis for disentangling data using linear regression modeling, transformations, interpretations of coefficients, and generalized linear models.
Covers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.
