machine-learning-pipelines

[UNMAINTAINED] Automated machine learning for analytics & production

(AAAI' 20) A Python Toolbox for Machine Learning Model Combination

A New, Interactive Approach to Learning Data Science

Machine learning pipelines for R.

Distributed Machine Learning Patterns (Manning Publications). Now available for early access. Check it out! https://bit.ly/2RKv8Zo

Primitives for machine learning and data science.

Provenance and caching library for python functions, built for creating lightweight machine learning pipelines

Exemplary, annotated machine learning pipeline for any tabular data problem.

kubeflow example

Improved pipelines for data science projects.

Wind Power Forecasting using Machine Learning techniques.

This project provides a machine learning pipeline to predict terrorist attack.

Library for comparison of different NLP machine learning pipelines with different backends.

Predict the customer flow (user payments) per day during the next 14 days for each shop on Koubei.com. Top 5% ranking solution for a Tianchi big data competition.

Sentiment analysis on customer reviews using machine learning and python

Create a machine learning pipeline, that categorizes disaster events.

Data Engineering Project of Udacity Data Scientist Nanodegree

This repository contains project files for a Flask app that classifies disaster messages into relevant categories.

Building machine learning pipelines with procedural programming, custom-pipeline or third-party code using the titanic data set from Kaggle

apply machine learning backup

This repository outlines a framework for building an anomaly detection algorithm and deploying into a web app

A basic classification model based on Random Forest Classifier predicting the Titanic Disaster Survival for a set of test data. Data Structure provided by Kaggle.

Creating a Machine Learning Pipeline to build and evaluate multiple models, using Python3

Machine Learning Tool to categorize messages that have been send after a disaster

Work with a set of Tweets about US airlines and examine their sentiment polarity.The aim is to learn to classify Tweets as either “positive”, “neutral”, or “negative” by using two classifiers and pipelines for pre-processing and model building.

During disaster events, sending messages to appropriate disaster relief agencies on a timely manner is critical. Using natural language processing and machine learning, I built a model for an API that classifies disaster messages and also a webapp for emergency works.

Sklearn compatible gridsearch based on a validation set.

Udacity Nanodegree Exercises and Projects

Implementation of Various Machine Learning(Supervised and Unsupervised) Algorithms