  Interactive Course

# 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 hours
• 16 Videos
• 59 Exercises
• 1,889 Participants
• 4,950 XP

### Loved by learners at thousands of top companies:      ### 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.

2. #### 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.

3. #### 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.

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.

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.

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. #### 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. #### 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.

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Decision Science Analytics @ USAA ##### 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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##### Collaborators
• Chester Ismay

• 