Linear Models: Classification Basics

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 3 of 4

Feature Expansion and Kernels

Covers feature expansion, kernels, SVM, and nonlinear classification in machine learning.

Feature Expansion and Kernel Methods

Explores feature expansion, kernel methods, SVM, and nonlinear classification in machine learning.

Classification Algorithms: Generative and Discriminative Approaches

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Textual Data Analysis: Classification & Dimensionality Reduction

Explores textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.

Linear Classification: Logistic Regression

Covers linear classification using logistic regression, regularization, and multiclass classification.

Linear Models: Continued

Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.

Support Vector Machine and Logistic Regression

Explains support vector machine and logistic regression for classification tasks, emphasizing margin maximization and risk minimization.

Logistic Regression: Probabilistic Interpretation

Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.

Logistic Regression: Fundamentals and Applications

Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.

Supervised Learning: Classification Algorithms

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.