Linear Models for Classification

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Linear Models for Classification: Multi-Class Extensions

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

Linear Models: Recap and Logistic Regression

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

Linear and Logistic Regression

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

Linear Models for Classification: Logistic Regression and SVM

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

Linear Models: Continued

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

Linear Models for Classification

Explores linear models for classification, logistic regression, and gradient descent in machine learning.

Logistic Regression: Fundamentals and Applications

Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.

Linear Models: Continued

Explores linear models, regression, multi-output prediction, classification, non-linearity, and gradient-based optimization.

Linear Regression and Logistic Regression

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

Linear Classification Models: From Binary to Multiclass

Explores the extension of linear classifiers to handle multiclass problems and compares their performance on various datasets.