msb-public
/
ML-For-Beginners
mirror of https://github.com/microsoft/ML-For-Beginners.git
1
0
Code Issues Projects Releases Wiki Activity
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from 'f16880941b'
${ noResults }
ML-For-Beginners/2-Regression
History
kenya-sk f16880941b
add japanese translation of Regression/3-Linear 		3 years ago
..
1-Tools add japanese translation of Regression/3-Linear 3 years ago
2-Data add japanese translation of Regression/3-Linear 3 years ago
3-Linear add japanese translation of Regression/3-Linear 3 years ago
4-Logistic Merge pull request #207 from fribble186/main 3 years ago
data re-numbering lesson groups 3 years ago
images re-numbering lesson groups 3 years ago
translations Merge pull request #220 from robertopauletto/main 3 years ago
README.md capitalization final audit 3 years ago

README.md

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!

jack-o-lanterns

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

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.