Logistic Regression: Probability Modeling

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Covers linear models for classification, focusing on logistic regression and support vector machines.

Explores logistic regression for binary classification, covering probability modeling, optimization methods, and regularization techniques.

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

Covers logistic regression, likelihood function, Newton's method, and classification error estimation.

Explores logistic regression, cost functions, gradient descent, and probability modeling using the logistic sigmoid function.

Covers fundamental concepts of probability and statistics, focusing on data analysis, graphical representation, and practical applications.

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Covers the theory behind Maximum Likelihood Estimation, discussing properties and applications in binary choice and ordered multiresponse models.

Covers jacamar data analysis, smoking data models, and challenges with log-linear models in visual impairment data.

Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.