Displaying 1 to 14 from 14 results

cortana-intelligence-customer360 - This repository contains instructions and code to deploy a customer 360 profile solution on Azure stack using the Cortana Intelligence Suite

  •    Python

The Customer 360 solution provides you a scalable way to build a customer profile enriched by machine learning. It also allows you to uniformly access and operate on data across disparate data sources (while minimizing raw data movement) and leverage the power of Microsoft R Server for scalable modelling and accurate predictions. Ingestion and Pre-processing: Ingest, prepare, and aggregate live user activity data.

machine_learning - machine learning applied to NLP without deep learning

  •    Python

The purpose of this respository is use machine learning to solve NLP problem without involving deep learning releted technology. So only traditional machine learning methods will be used here. It will include Naive Bayes, Decision Tree, Random Forest,GBDT and so on. We will first use Naive Bayes to do binary classification, which is to classify a sentence releted to be a 'theft' or not.

GENESIM - An innovative technique that constructs an ensemble of decision trees and converts this ensemble into a single, interpretable decision tree with an enhanced predictive performance

  •    Scilab

A wrapper is written around Orange C4.5, sklearn CART, GUIDE and QUEST. The returned object is a Decision Tree, which can be found in decisiontree.py. Moreover, different methods are available on this decision tree: classify new, unknown samples; visualise the tree; export it to string, JSON and DOT; etc. A wrapper written around the R package inTrees and an implementation of ISM can be found in the constructors package.

leaves - pure Go implementation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks

  •    Go

leaves is a library implementing prediction code for GBRT (Gradient Boosting Regression Trees) models in pure Go. The goal of the project - make it possible to use models from popular GBRT frameworks in Go programs without C API bindings. In order to use XGBoost model, just change leaves.LGEnsembleFromFile, to leaves.XGEnsembleFromFile.

aifad - AIFAD - Automated Induction of Functions over Algebraic Data Types

  •    OCaml

AIFAD stands for Automated Induction of Functions over Algebraic Data Types and is an application written in OCaml that improves decision tree learning by supporting significantly more complex kinds of data. This allows users to more conveniently describe the data they want to learn functions on and can improve the accuracy and complexity of resulting models. Handles multi-valued attributes. This has already become widespread among decision tree learners, but some implementations still only support binary ones.

Machine_Learning - Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks

  •    Jupyter

Esse repositório foi criado com a intenção de difundir o ensino de Machine Learning em português. Os algoritmos aqui implementados não são otimizados e foram implementados visando o fácil entendimento. Portanto, não devem ser utilizados para fins de pesquisa ou outros fins além dos especificados.

AdaptiveRandomForest - Repository for the AdaptiveRandomForest algorithm implemented in MOA 2016-04

  •    Java

Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems.

goscore - Go Scoring API for PMML

  •    Go

Go scoring API for Predictive Model Markup Language (PMML). Will be happy to implement new models by demand, or assist with any other issue.

2018-MachineLearning-Lectures-ESA - Machine Learning Lectures at the European Space Agency (ESA) in 2018

  •    Jupyter

In 2018, The European Space Agency (ESA) organized a series of 6 lectures on Machine Learning at the European Space Operations Centre (ESOC). This repository contains the lectures resources: presentations, notebooks and links to the videos (presentation and hands-on).