Author: Shengxuan Wang
From: Oregon State University
Building Machine Learning model from Scratch, without using any ML package. Only use data processing package, e.g. Pandas and Numpy, and a package I made: Popanda (see the detail here: https://github.com/shawn120/Popanda_Enhance_of_Pandas) Then using some data set to test them.
Models include Linear Regression, Logistic Regression, Perceptron, and Decision Tree.
Katana ML Skipper This is a simple and flexible ML workflow engine. It helps to orchestrate events across a set of microservices and create executable
Nevergrad - A gradient-free optimization platform nevergrad is a Python 3.6+ library. It can be installed with: pip install nevergrad More installati
pyGAM Generalized Additive Models in Python. Documentation Official pyGAM Documentation: Read the Docs Building interpretable models with Generalized
A Microsoft Azure Web App project named Covid 19 Predictor using Machine learning Model (Random Forest Classifier Model ) that helps the user to identify whether someone is showing positive Covid sym
Machine care An simple python script to take care of simple maintenance tasks fo
Model-Based-Recommender-Engine I created a book Recommender System using Sci-kit learn's N-neighbours algorithm for my model and the streamlit library
sk-dist: Distributed scikit-learn meta-estimators in PySpark What is it? sk-dist is a Python package for machine learning built on top of scikit-learn
PyDLM Welcome to pydlm, a flexible time series modeling library for python. This library is based on the Bayesian dynamic linear model (Harrison and W
Py-boost: a research tool for exploring GBDTs Modern gradient boosting toolkits are very complex and are written in low-level programming languages. A
Graph-based CTR prediction This is a repository designed for graph-based CTR prediction methods, it includes our graph-based CTR prediction methods: F
Disclaimer: This package is WIP. Do not take any APIs for granted. tspreprocess Time series can contain noise, may be sampled under a non fitting rate
Multiple-Linear-Regression-master - A python program to implement Multiple Linear Regression using the LinearRegression class from sklearn.linear model library
A powerful and flexible machine learning platform for drug discovery
napari-sklearn-decomposition A simple plugin to use with napari This napari plug
Simple but powerful Automated Machine Learning library for tabular data. It uses efficient in-memory SAP HANA algorithms to automate routine Data Scie
GluonTS - Probabilistic Time Series Modeling in Python GluonTS is a Python toolkit for probabilistic time series modeling, built around Apache MXNet (
Model Validation Toolkit is a collection of tools to assist with validating machine learning models prior to deploying them to production and monitoring them after deployment to production.
Neural Machine Translation (NMT) tutorial with OpenNMT-py. Data preprocessing, model training, evaluation, and deployment.
Time-series-momentum Time-series momentum strategy. You can use the data_analysis.py file to find out the best trigger and window for a given asset an
torque_model The torque model is a spiritual successor to op-smart-torque, which was a project to train a neural network to control a car's steering f
8 Nov 17, 2022
3.4k Jan 08, 2023
747 Jan 05, 2023
2 Oct 06, 2022
2 Jul 10, 2022
1 Jan 13, 2022
282 Dec 09, 2022
438 Dec 17, 2022
20 Sep 21, 2022
47 Dec 30, 2022
57 Dec 17, 2022
1 Feb 06, 2022
1.1k Jan 08, 2023
1 Sep 01, 2022
47 Dec 17, 2022
3.3k Jan 03, 2023
25 Dec 28, 2022
29 Jan 09, 2023
3 Jun 18, 2022
4 Jun 03, 2022