K-Nearest Neighbors & Feature Expansion

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Introduces k-Nearest Neighbors method and feature expansion for nonlinear machine learning through polynomial transformations.

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

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

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

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

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

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.

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

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