Linear Models for Classification: Multi-Class Extensions

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Linear Models: Recap and Logistic Regression

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

Generalized Linear Regression: Classification

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

Linear Models for Classification: Part 3

Explores linear models for classification, including binary classification, logistic regression, decision boundaries, and support vector machines.

Linear Models: Classification

Explores linear models for classification, including logistic regression, decision boundaries, and support vector machines.

Linear Classification Models: From Binary to Multiclass

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

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

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

Logistic Regression: Fundamentals and Applications

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

Nonlinear Machine Learning: k-Nearest Neighbors and Feature Expansion

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Linear and Logistic Regression

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