Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Mars | 2,664 | 1 | 6 months ago | 118 | June 12, 2022 | 212 | apache-2.0 | Python | ||
Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions. | ||||||||||
Python Tutorial | 1,261 | a year ago | 1 | apache-2.0 | Jupyter Notebook | |||||
Python实用教程,包括:Python基础,Python高级特性,面向对象编程,多线程,数据库,数据科学,Flask,爬虫开发教程。 | ||||||||||
Alphapy | 1,003 | 6 months ago | 25 | August 29, 2020 | 13 | apache-2.0 | Python | |||
Automated Machine Learning [AutoML] with Python, scikit-learn, Keras, XGBoost, LightGBM, and CatBoost | ||||||||||
Xorbits | 946 | 3 | 6 months ago | 25 | November 21, 2023 | 117 | apache-2.0 | Python | ||
Scalable Python DS & ML, in an API compatible & lightning fast way. | ||||||||||
Eland | 588 | 3 | 5 months ago | 30 | November 22, 2023 | 88 | apache-2.0 | Python | ||
Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch | ||||||||||
Bdci2018 Chinauuicom 1st Solution | 271 | 6 years ago | 1 | Python | ||||||
这是BDCI2018的联通赛题第一名解决方案 | ||||||||||
Feature Engineering Tutorials | 217 | 2 years ago | 5 | agpl-3.0 | Jupyter Notebook | |||||
Data Science Feature Engineering and Selection Tutorials | ||||||||||
Nba Prediction | 186 | 8 months ago | 1 | Jupyter Notebook | ||||||
A project to deploy an online app that predicts the win probability for each NBA game every day. Demonstrates end-to-end Machine Learning deployment. | ||||||||||
Tensorflow Ml Nlp | 157 | 4 years ago | 5 | Jupyter Notebook | ||||||
텐서플로우와 머신러닝으로 시작하는 자연어처리(로지스틱회귀부터 트랜스포머 챗봇까지) | ||||||||||
30 Days Of Ml Kaggle | 93 | 3 years ago | mit | Jupyter Notebook | ||||||
Machine learning beginner to Kaggle competitor in 30 days. Non-coders welcome. The program starts Monday, August 2, and lasts four weeks. It's designed for people who want to learn machine learning. |