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 '056f9a73b1'
${ noResults }
ML-For-Beginners/2-Regression
History
Jen Looper 056f9a73b1
classification 2 		3 years ago
..
1-Tools moving graphics around 3 years ago
2-Data Assignment callout made more clear 3 years ago
3-Linear edit to graphic 3 years ago
4-Logistic classification 2 3 years ago
data re-numbering lesson groups 3 years ago
images re-numbering lesson groups 3 years ago
README.md links to Learn added 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

In this section, 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

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.