Daya-Jin/ML_for_learner
Implementations of the machine learning algorithm with Python and numpy
This project offers a practical way for students and self-learners to understand core machine learning algorithms. It provides detailed explanations of how algorithms like Linear Regression, K-means, and Decision Trees work, along with step-by-step code implementations. Learners can input data as lists or NumPy arrays and observe how these algorithms process information and produce results, fostering a deeper understanding beyond just using pre-built libraries.
No commits in the last 6 months.
Use this if you are a student or aspiring data scientist who wants to learn the fundamental theories behind machine learning algorithms and understand how they are coded from scratch.
Not ideal if you need a robust, production-ready machine learning library for complex real-world applications or if you are solely interested in applying pre-built models without delving into their internal mechanics.
Stars
86
Forks
44
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Oct 20, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Daya-Jin/ML_for_learner"
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