Generalized Linear Models Courses and Certifications

Simons Institute

Sparsifying Generalized Linear Models

Explores sparsification of sums in generalized linear models, presenting algorithms for efficient computation and applications to optimization problems like regression.

• YouTube
• 56 minutes
• On-Demand
• Free Video

UofU Data Science

Generalized Linear Models and Linear Regression - Lecture 11

Dive into advanced statistical concepts including generalized linear models, nonlinear basis functions, and regularization techniques for robust data analysis and point estimation.

• YouTube
• 1 hour 19 minutes
• On-Demand
• Free Video

Generalized Linear Models in R

The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Generalized Linear Models in Python

Extend your regression toolbox with the logistic and Poisson models and learn to train, understand, and validate them, as well as to make predictions.

• DataCamp
• 5 hours
• On-Demand
• Free Trial Available

Simons Institute

Estimating Separable Matching Models

Explores methods for estimating separable matching models with transferable utility, focusing on minimum distance estimation and reformulation of the Choo and Siow model as a generalized linear model with two-way fixed effects.

• YouTube
• 27 minutes
• On-Demand
• Free Video

NPTEL-NOC IITM

Generalised Linear Models: Introduction to Natural Exponential Family and Binomial Distributions

Explore Generalised Linear Models and Natural Exponential Family, focusing on Binomial Distributions and their applications in statistical modeling.

• YouTube
• 19 minutes
• On-Demand
• Free Video

商管統計資料分析三 (Analytics for Business and Management (III))

Master advanced statistical modeling for binary and count data using R, covering logistic regression, log-linear models, GLM framework, and overdispersion handling with business cases.

• Coursera
• 8 hours 59 minutes
• On-Demand
• Paid Course

Multiple and Logistic Regression in R

In this course you'll learn to add multiple variables to linear models and to use logistic regression for classification.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Pascal Poupart

Logistic Regression and Generalized Linear Models

Explore exponential family models, logistic regression, and Newton's method for classification tasks, with applications in app recommendation and sparse data analysis.

• YouTube
• 1 hour 35 minutes
• On-Demand
• Free Video

Generalized Linear Models and Nonparametric Regression

21 ratings at Coursera

Explore advanced statistical modeling tools including GLMs, nonparametric regression, and GAMs. Learn to apply these techniques for classification, data analysis, and ethical decision-making in data science.

• Coursera
• 1 day 18 hours 4 minutes
• On-Demand
• Paid Course

Principles of fMRI 1

850 ratings at Coursera

This course covers the design, acquisition, and analysis of Functional Magnetic Resonance Imaging (fMRI) data. A book related to the class can be found here: https://leanpub.com/principlesoffmri

• Coursera
• 8-9 hours
• On-Demand
• Paid Course

BIMSA

χ2-Statistics for High-Dimensional Inference of Multivariate Generalized Linear Models

Discover χ2-statistics applications for high-dimensional inference in multivariate generalized linear models through advanced statistical methodology.

• YouTube
• 53 minutes
• On-Demand
• Free Video

statisticsmatt

Generalized Linear Models

Comprehensive exploration of Generalized Linear Models, covering various regression techniques including Probit, Logistic, Complementary Log-Log, Poisson, Ordinal Logistic, and Multinomial Logistic.

• YouTube
• 3 hours 30 minutes
• On-Demand
• Free Video

Statistics for Applications

1 review

This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications.

• MIT OpenCourseWare
• 28 hours
• On-Demand
• Free Video

Variable Selection, Model Validation, Nonlinear Regression

Master advanced regression techniques for data-driven industries. Learn logistic, Poisson, and robust regression, generalized linear models, and model validation using R for practical applications.

• Coursera
• 20 hours 43 minutes
• On-Demand
• Paid Course