NonLinear Regression

Related lectures (31)

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Explores linear regression using the method of least squares to fit data points with the equation y = ax + b.

Explores the IRLS algorithm for weighted least squares estimation in GLM.

Covers linear regression, regularization, and probabilistic models in generating labels.

Explores advanced concepts in linear regression models, including multicollinearity, hypothesis testing, and handling outliers.

Introduces regression analysis for multivariate data modeling, covering matrix algebra, interpretation of coefficients, and test intervals.

Covers the basics of linear regression, focusing on estimation and prediction.

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.

Covers supervised learning, classification using logistic regression, and challenges in optimization.

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.