Home Projects machine_learning_examples
machine_learning_examples
Python

machine_learning_examples

A collection of machine learning examples and tutorials.

by lazyprogrammer · GitHub
Stars
Forks
Created
Last commit
Category
Language
data-sciencedeep-learningmachine-learningPython
View on GitHub
In plain words

Explore various examples and tutorials to learn about machine learning techniques.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
machine_learning_examples — GitHub preview card
📈 Star history
8.90k8.89k
2026-07-042026-08-31
📈 Track machine_learning_examples

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

A collection of machine learning examples and tutorials.

machine_learning_examples has 8.9k stars on GitHub. It has been forked 6.4k times. machine_learning_examples is written mainly in Python. It has been in active development since 2014. Its main topics are data-science, deep-learning, machine-learning, natural-language-processing.

Frequently asked questions

What is machine_learning_examples?

A collection of machine learning examples and tutorials.

Is machine_learning_examples open source?

machine_learning_examples is an open-source project.

Is machine_learning_examples free?

Yes. machine_learning_examples is free and open source — you can use, modify and self-host it.

What language is machine_learning_examples written in?

machine_learning_examples is written mainly in Python.

🏅 Maintainer of this project?
olud.ai badge — machine_learning_examples

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=lazyprogrammer-machine-learning-examples)](https://olud.ai/project/lazyprogrammer-machine-learning-examples.html)
More badge options →
🧬 Shares DNA with

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.