kochlisGit/Advanced-ML
Advanced Machine Learning Algorithms including Cost-Sensitive Learning, Class Imbalances, Multi-Label Data, Multi-Instance Learning, Active Learning, Multi-Relational Data Mining, Interpretability in Python using Scikit-Learn.
This project helps data scientists, machine learning engineers, and researchers tackle complex real-world data challenges by providing advanced machine learning techniques. It takes your datasets with issues like uneven categories, multiple labels, or grouped data, and outputs more accurate and robust predictive models. Anyone building advanced AI solutions will find this useful for improving model performance on tricky data.
No commits in the last 6 months.
Use this if you are a data scientist or machine learning practitioner struggling with common, difficult data characteristics such as imbalanced classes, multi-label classifications, or grouped data in your machine learning projects.
Not ideal if you are looking for basic machine learning model implementations or tools for data visualization and preprocessing before model training.
Stars
9
Forks
1
Language
Jupyter Notebook
License
—
Category
Last pushed
May 01, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/kochlisGit/Advanced-ML"
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