Support Vector Machines: Non-Linear Data Mapping

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

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

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

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.

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

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 textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

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