Explores Kernel Ridge Regression, the Kernel Trick, Representer Theorem, feature spaces, kernel matrix, predicting with kernels, and building new kernels.
Explores Principal Component Analysis for dimensionality reduction in machine learning, showcasing its feature extraction and data preprocessing capabilities.
Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.
Explores kernels for simplifying data representation and making it linearly separable in feature spaces, including popular functions and practical exercises.