Kernel Methods: SVM and Regression

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Machine Learning Fundamentals: Regularization and Cross-validation

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.

Unsupervised Learning: Dimensionality Reduction

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.

Kernel Methods and Regression

Covers kernel methods, kernel regression, RBF kernel, and SVM for classification.

Machine Learning Fundamentals

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

Linear Models & k-NN

Covers linear models, logistic regression, decision boundaries, k-NN, and practical applications in authorship attribution and image data analysis.

Feature Expansion: Kernels and KNN

Covers feature expansion, kernels, and K-nearest neighbors, including non-linearity, SVM, and Gaussian kernels.

Kernel Methods in Machine Learning: Kernel Regression and SVM

Discusses kernel methods in machine learning, focusing on kernel regression and support vector machines, including their formulations and applications.

Feature Selection, Kernel Regression, Neural Networks Playground

Covers feature selection, kernel regression, and neural networks through exercises.

Kernel Methods: Machine Learning

Explores kernel methods in machine learning, emphasizing their application in regression tasks and the prevention of overfitting.

Linear Models: Classification Basics

Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.