ML-For-Beginners/translations/en/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 spooky faces for Halloween. Let's explore more about these intriguing vegetables!

Photo by Beth Teutschmann on Unsplash

What you will learn

🎥 Click the image above for a quick introduction video to this lesson

The lessons in this section focus on types of regression within the context of machine learning. Regression models are useful for identifying the relationship between variables. These models can predict values like length, temperature, or age, helping to uncover patterns and relationships as they analyze data points.

In this series of lessons, you'll learn the differences between linear and logistic regression, and understand when to use one over the other.

🎥 Click the image above for a short video introducing regression models.

In this set of lessons, you'll prepare to start machine learning tasks, including setting up Visual Studio Code to manage notebooks, a common environment for data scientists. You'll explore Scikit-learn, a machine learning library, and build your first models, focusing on regression models in this chapter.

There are helpful low-code tools available to assist you in learning about regression models. Check out 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 created with ♥️ by Jen Looper

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

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

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Disclaimer:
This document has been translated using the AI translation service Co-op Translator. While we aim for accuracy, please note that automated translations may include errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is advised. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.