Covers the concept of kernel regression and making data linearly separable by adding features and using local methods.
Explores kernels for simplifying data representation and making it linearly separable in feature spaces, including popular functions and practical exercises.
Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.
Covers overfitting, model selection, validation methods, kernel functions, and SVM concepts.
Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.
