Linear Models: ClassificationExplores linear models for classification, including logistic regression, decision boundaries, and support vector machines.
Machine Learning FundamentalsIntroduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Linear Models: Part 2Covers linear models, binary and multi-class classification, and logistic regression with practical examples.
Nonlinear ML AlgorithmsIntroduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
Linear Models for ClassificationExplores linear models for classification, logistic regression, decision boundaries, SVM, multi-class classification, and practical applications.
Supervised Learning EssentialsIntroduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Regression: Linear ModelsIntroduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
