Linear Models for Classification

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Related lectures (31)

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Linear Models for Classification: Logistic Regression and SVM

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

Linear Models for Classification: Multi-Class Extensions

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Logistic Regression: Cost Functions & Optimization

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

Linear Models: Continued

Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.

Logistic Regression: Vegetation Prediction

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

Linear Models: Recap and Logistic Regression

Covers linear models, binary classification, logistic regression, and model evaluation metrics.

Optimization in Machine Learning: Gradient Descent

Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.

Linear Models for Classification

Covers linear models for classification, logistic regression training, evaluation metrics, and decision boundaries.

Linear Regression and Logistic Regression

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Linear and Logistic Regression

Introduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.