Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Practice
Publications
Startups
Units
Show all results for
Home
Lecture
Generalized Linear Models: Examples and Applications
Graph Chatbot
Related lectures (31)
Supervised Learning Fundamentals
Introduces the fundamentals of supervised learning, including loss functions and probability distributions.
Modern Regression: Overdispersion and Model Assessment
Explores overdispersion, model assessment, and regression techniques for count data.
Linear Models for Classification: Multi-Class Extensions
Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.
Linear Models: Continued
Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.
Regression: Linear Models
Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
Linear Models for Classification
Covers linear models for classification, logistic regression training, evaluation metrics, and decision boundaries.
Parametric Models
Explores statistical estimation, regression models, and model selection in parametric models.
Nonlinear Machine Learning: k-Nearest Neighbors and Feature Expansion
Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.
Logistic Regression: Statistical Inference and Machine Learning
Covers logistic regression, likelihood function, Newton's method, and classification error estimation.
Linear Models for Classification
Explores linear models for classification, logistic regression, and gradient descent in machine learning.
Logistic Regression: Fundamentals and Applications
Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.
Previous
Page 2 of 2
Next