Jack-Cherish/Machine-Learning
:zap:机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归
This project offers practical, code-based examples for various machine learning algorithms, helping you understand how to apply them to real-world data. It takes raw datasets, processes them using different algorithms like kNN, Decision Trees, and Support Vector Machines, and outputs predictions or classifications. Anyone who needs to analyze data to make predictions or categorize information, such as data analysts, researchers, or business intelligence professionals, would find this useful.
10,250 stars. No commits in the last 6 months.
Use this if you are a data professional looking for hands-on examples to understand and implement fundamental machine learning algorithms for tasks like classification or prediction.
Not ideal if you need a plug-and-play solution for advanced, large-scale, or highly specific machine learning applications without diving into the code.
Stars
10,250
Forks
5,108
Language
Python
License
—
Category
Last pushed
Jul 12, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Jack-Cherish/Machine-Learning"
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