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.

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5 Hours16 Videos59 Exercises5,906 Learners
4950 XP

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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

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

Chester IsmayAdrián Soto
Ita Cirovic Donev Headshot

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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