跳至内容
首页Python

课程

Generalized Linear Models in Python

高级技能水平
更新时间 2022年10月
Extend your regression toolbox with the logistic and Poisson models and learn to train, understand, and validate them, as well as to make predictions.
免费开始课程
PythonProbability & Statistics5 小时16 视频59 练习4,950 经验值11,833成就声明

创建您的免费帐户

或

继续操作即表示您接受我们的《使用条款》和《隐私政策》，并同意您的数据存储在美国。

深受数千家公司学习者的喜爱

Group

培训2人或更多？

试用DataCamp for Business

课程描述

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.

先决条件

Introduction to Linear Modeling in Python
1

Introduction to GLMs

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.
开始章节
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.
开始章节
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.
开始章节
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.
开始章节
Generalized Linear Models in Python
课程完成

获得成就证明

将此证书添加到你的 LinkedIn 档案、简历或履历中
在社交媒体和绩效评估中分享立即注册

加入超过19百万学习者，今天就开始Generalized Linear Models in Python！

创建您的免费帐户

或

继续操作即表示您接受我们的《使用条款》和《隐私政策》，并同意您的数据存储在美国。

通过 DataCamp for Mobile 提升您的数据技能

随时随地通过我们的移动课程和每日 5 分钟编程挑战提升技能。