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Design Effective Statistical Models to Understand Your Data
Linear regression is the go-to method to rapidly understand a dataset.
Linear and logistic regression are prevailing and powerful statistical methods used in a variety of domains. They are simple to implement, yet offer a depth of analytical knowledge. What's more, they can be used both for statistical modeling and predictive analytics.
This course is designed to teach you applied linear and logistic regression by having you (yes, you!) build increasingly complex models. First, you will gain a deep understanding of the foundations before diving into linear regression. After that, we will go one step further and handle classification and nonlinear datasets. Finally, we will use regression techniques to build resilient predictive models.
Throughout the course, we will work on real-world datasets to give you tangible, hands-on training.
Ready to go?
Learning outcomes
- Build Linear Regression Models
- Build Generalized Linear Models
- Build Predictive Models
Turn it into a career
Take one of our 100% online diploma training programs, and transform your know-how into professional skills.
- Up to 100% of your training program funded
- Flexible start date
- Career-focused projects
- Individual mentoring
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Table of contents
- Part 1
Understand the Fundamentals of Statistical Modeling
- Part 2
Build Linear Regression Models
- Part 3
Build Generalized Linear Models
- Part 4
Build Resilient Predictive Models
Contributors
Instructor
Alexis Perrier
Auteur et enseignant en Data Science, expert Machine Learning. Suivez @alexip sur Twitter.Created by

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