M0hc3n/Machine-Learning-Algorithms-From-Scratch
This repository gathers the essential Machine Learning algorithms coded from scratch using only numpy and sklearn
This project provides foundational machine learning algorithms implemented from scratch, using only basic Python libraries. It takes in sample datasets, typically generated for testing purposes, and outputs the results of various machine learning models like accuracy scores or data visualizations. This is a resource for machine learning students, educators, and developers who want to understand or teach the core mechanics of these algorithms.
No commits in the last 6 months.
Use this if you are a developer, student, or instructor who needs to deeply understand how machine learning algorithms work under the hood without relying on complex frameworks.
Not ideal if you are a practitioner looking for a ready-to-use library to apply machine learning to real-world datasets or for high-performance production systems.
Stars
14
Forks
—
Language
Python
License
—
Category
Last pushed
Oct 10, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/M0hc3n/Machine-Learning-Algorithms-From-Scratch"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
uxlfoundation/scikit-learn-intelex
Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
INRIA/scikit-learn-mooc
Machine learning in Python with scikit-learn MOOC
ddbourgin/numpy-ml
Machine learning, in numpy
nubank/fklearn
fklearn: Functional Machine Learning
gavinkhung/machine-learning-visualized
ML algorithms implemented and derived from first-principles in Jupyter Notebooks and NumPy