Linear Models: ClassificationExplores linear models for classification, including logistic regression, decision boundaries, and support vector machines.
Linear Models & k-NNCovers linear models, logistic regression, decision boundaries, k-NN, and practical applications in authorship attribution and image data analysis.
Multiclass ClassificationCovers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.
Linear and Logistic RegressionIntroduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.
Deep Learning FundamentalsIntroduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
Supervised Learning EssentialsIntroduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Linear Models: Part 2Covers linear models, binary and multi-class classification, and logistic regression with practical examples.
