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.

Going beyond linear regression

Applying linear models

Linear model, a special case of GLM

How to build a GLM?

Data type and distribution family

Linear model and a binary response variable

Comparing predicted values

How to fit a GLM in Python?

Model fitting step-by-step

Results of the model fit using summary()

Extracting parameter estimates

Introduction to GLMs

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.

Binary data and logistic regression

Compute odds and probabilities

Fit logistic regression

Interpreting coefficients

Coefficients in terms of odds

Model formula

Interpreting logistic model

Rate of change in probability

Interpreting model inference

Statistical significance

Computing Wald statistic

Confidence intervals

Computing and describing predictions

Visualize model fit using regplot()

Compute predictions

Compute confusion matrix

Modeling Binary 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.

Count data and Poisson distribution

Visualize the response

Fitting a Poisson regression

Interpreting model fit

Estimate parameter lambda

Interpret Poisson coefficients

Poisson confidence intervals

The Problem of Overdispersion

Is the mean equal to the variance?

Computing expected number of counts

Checking for overdispersion

Fitting negative binomial

Confidence intervals for negative Binomial model

Plotting a regression model

Plotting data and linear model fit

Plotting fitted values

Modeling Count Data

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.

Multivariable logistic regression

Fit a multivariable logistic regression

The effect of multicollinearity

Compute VIF

Comparing models

Checking model fit

Compare two models

Deviance and linear transformation

Model matrix for continuous variables

Variable transformation

Coding categorical variables

Categorical and interaction terms

Modeling with categorical variable

Interaction terms

Congratulations!

Multivariable Logistic Regression

Well switch due to arsenic poisoning

Nesting of the female horseshoe crab

Credit default

Level of salary and years of work experience

Medical costs per person given age and BMI

Bike crossings in New York City

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.

Introduction to Linear Modeling 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.

Generalized Linear Models in Python

Data Science Consultant

statsmodels

Process of model fit

Describing the model

Formula Argument

Family Argument

Summarizing the model

Regression coefficients

Predictions

Let's practice!

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Introduction to GLMs - How to fit a GLM in Python?

How to fit a GLM in Python?

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