University of Colorado Boulder - Generalized Linear Models and Nonparametric Regression
- Offered byCoursera
Generalized Linear Models and Nonparametric Regression at Coursera Overview
Duration | 42 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Generalized Linear Models and Nonparametric Regression at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 3 of 3 in the Statistical Modeling for Data Science Applications Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level Calculus, linear algebra, and probability theory.
- Approx. 42 hours to complete
- English Subtitles: English
Generalized Linear Models and Nonparametric Regression at Coursera Course details
- In the final course of the statistical modeling for data science program, learners will study a broad set of more advanced statistical modeling tools. Such tools will include generalized linear models (GLMs), which will provide an introduction to classification (through logistic regression); nonparametric modeling, including kernel estimators, smoothing splines; and semi-parametric generalized additive models (GAMs). Emphasis will be placed on a firm conceptual understanding of these tools. Attention will also be given to ethical issues raised by using complicated statistical models.
- This course can be taken for academic credit as part of CU Boulder's Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder's departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Generalized Linear Models and Nonparametric Regression at Coursera Curriculum
An Introduction to Generalized Linear Models Through Binomial Regression
From Linear Models to Generalized Linear Models
The Components of a GLM
The Exponential Family of Distributions
Introduction to Binomial Regression
Binomial Regression Parameter Estimation
Interpretation of Binomial Regression
Binomial Regression in R
FairML Book, Introduction
Introduction to Generalized Linear Models
Binomial Regression
Binomial Regression Inference
Models for Count Data
Poisson Regression: A New Model for Count Data
Poisson Regression Parameter Estimation
Interpreting the Poisson Regression Model
Poisson Regression on Real Data in R
Goodness of Fit for Poisson Regression I
Goodness of Fit for Poisson Regression II
Overdispersion
Poisson Regression Basics
Poisson Regression Inference and Goodness of Fit
Introduction to Nonparametric Regression
Introduction to Nonparametric Regression Models
Motivating Kernel Estimators
Kernel Estimators
Smoothing Splines
Loess: Locally Estimated Scatterplot Smoothing
Kernel Estimation in R
Nonparametric Regression: Theory
Introduction to Generalized Additive Models
Motivating Generalized Additive Models
Generalized Additive Models in R
Inference with Generalized Additive Models: Effective Degrees of Freedom
Inference with Generalized Additive Models: Tests
Generalized Additive Models in R: Inference and Interpretation
Generalized Additive Models: A Complete Example with Real Data
Required: Generalized additive models for data science
Generalized Additive Models: Basics
Generalized Additive Models: Inference and Data Analysis
Generalized Linear Models and Nonparametric Regression at Coursera Admission Process
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