K-Nearest Neighbors & Feature Expansion

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Introduces k-Nearest Neighbors for classification and feature expansion to handle nonlinear data through transformed inputs.

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

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

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

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

Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.

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

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

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

Explores linear regression in high dimensions and practical house price prediction from a dataset.