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 'dc1f63df8b'
${ noResults }
ML-For-Beginners/Regression
History
Tomomi ❤ Imura dc1f63df8b
Add a sketchnote that summarize the lessons 		4 years ago
..
1-Tools infographic for regression 4 years ago
2-Data Correcting spelling mistakes 4 years ago
3-Linear quiz links, quizzes 7 and 8, Regression Lesson 1 4 years ago
4-Logistic Merge branch 'main' of https://github.com/jlooper/ml-for-beginners into main 4 years ago
data renumbering all segments 4 years ago
images Add a sketchnote 4 years ago
README.md Add a sketchnote that summarize the lessons 4 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.

Lessons

  1. Tools of the Trade
  2. Managing Data
  3. Linear and Polynomial Regression
  4. Logistic Regression

Summary of the lessons

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.