# 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.

5 Hours16 Videos59 Exercises

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## Course Description

Imagine being able to handle data where the response variable is either binary, count, or approximately normal, all under one single framework. Well, you don't have to imagine. Enter the Generalized Linear Models in Python course! In this course you will extend your regression toolbox with the logistic and Poisson models, by learning how to fit, understand, assess model performance and finally use the model to make predictions on new data. You will practice using data from real world studies such the largest population poisoning in world's history, nesting of horseshoe crabs and counting the bike crossings on the bridges in New York City.
1. 1

### Introduction to GLMs

Free

Review linear models and learn how GLMs are an extension of the linear model given different types of response variables. You will also learn the building blocks of GLMs and the technical process of fitting a GLM in Python.

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Going beyond linear regression
50 xp
Applying linear models
50 xp
Linear model, a special case of GLM
100 xp
How to build a GLM?
50 xp
Data type and distribution family
100 xp
Linear model and a binary response variable
100 xp
Comparing predicted values
100 xp
How to fit a GLM in Python?
50 xp
Model fitting step-by-step
100 xp
Results of the model fit using summary()
100 xp
Extracting parameter estimates
100 xp
2. 2

### Modeling Binary Data

This chapter focuses on logistic regression. You'll learn about the structure of binary data, the logit link function, model fitting, as well as how to interpret model coefficients, model inference, and how to assess model performance.

3. 3

### Modeling Count Data

Here you'll learn about Poisson regression, including the discussion on count data, Poisson distribution and the interpretation of the model fit. You'll also learn how to overcome problems with overdispersion. Finally, you'll get hands-on experience with the process of model visualization.

4. 4

### Multivariable Logistic Regression

In this final chapter you'll learn how to increase the complexity of your model by adding more than one explanatory variable. You'll practice with the problem of multicollinearity, and with treating categorical and interaction terms in your model.

Datasets

Well switch due to arsenic poisoningNesting of the female horseshoe crabCredit defaultLevel of salary and years of work experienceMedical costs per person given age and BMIBike crossings in New York City

Collaborators

Ita Cirovic Donev

Data Science consultant

Ita is a Data Science consultant. She spends her time finding stories in data and developing predictive models for credit risk using machine learning methods. With the experience of over 15 years, she has worked on diverse problems with many interestingly complex datasets, ranging from loan repayment behavior to a person's spending behavior. Her free time is usually spent in bookstores or reading books.
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