neerjad/MachineLearning
A repo with tutorials for algorithms from scratch
This collection of tutorials helps data scientists and machine learning engineers understand the core mechanics of common algorithms. It takes you through building machine learning models like linear regression, k-means, and decision trees from the ground up. You'll gain a deeper understanding of how these algorithms process raw data to produce predictions or insights.
102 stars. No commits in the last 6 months.
Use this if you are a data scientist or machine learning engineer who wants to learn the fundamental mathematical and algorithmic concepts behind common machine learning models without relying on high-level libraries.
Not ideal if you're looking for a production-ready library to quickly apply machine learning models to your datasets or if you're not comfortable with coding.
Stars
102
Forks
21
Language
Jupyter Notebook
License
—
Category
Last pushed
Jun 09, 2018
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/neerjad/MachineLearning"
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