Linear Regression and Logistic Regression

Related lectures (29)

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Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Covers linear and logistic regression, including underfitting, overfitting, and performance metrics.

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.

Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.