ML-For-Beginners/2-Regression

Regression models for machine learning

Regional topic: Regression models for pumpkin prices in North America 🎃

In North America, pumpkins are often carved into scary faces for Halloween. Let's discover more about these fascinating vegetables!

Photo by Beth Teutschmann on Unsplash

What you will learn

The lessons in this section cover types of regression in the context of machine learning. Regression models can help determine the relationship between variables. This type of model can predict values such as length, temperature, or age, thus uncovering relationships between variables as it analyzes data points.

In this series of lessons, you'll discover the difference between linear vs. logistic regression, and when you should use one or the other.

In this group of lessons, you will get set up to begin machine learning tasks, including configuring Visual Studio code to manage notebooks, the common environment for data scientists. You will discover Scikit-learn, a library for machine learning, and you will build your first models, focusing on Regression models in this chapter.

There are useful low-code tools that can help you learn about working with regression models. Try Azure ML for this task

Lessons

1. Tools of the trade
2. Managing data
3. Linear and polynomial regression
4. Logistic regression

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Credits

"ML with regression" was written with ♥️ by Jen Looper

♥️ Quiz contributors include: Muhammad Sakib Khan Inan and Ornella Altunyan

The pumpkin dataset is suggested by this project on Kaggle and its data is sourced from the Specialty Crops Terminal Markets Standard Reports distributed by the United States Department of Agriculture. We have added some points around color based on variety to normalize the distribution. This data is in the public domain.