Synthetic Data Generator Tool: UnReal
About the app Demonstration User Plans Pricing Feedback guidelines Frequently Asked Questions App status Go to about section Demo Go to Plans Its free!! 🎉 Feature Request FAQs Developing About the app This application helps in generating synthetic data in a fast and easy way. Currently, the data corresponds to retail domain but there are plans of adding more domains as well.
Documentation of python package: indepth
This is the official documentation of Python package indepth and is currently a work in progress document. Please come back later to find the completed documentation. Table of contents Overview Installation remov_punct wrdCnt vocabSize commonWrd realWrds MostSimilarSent sbjVrbAgreement modalRuleError PrpDonot VrbTenseAgreementError a_an_error motionVerbs coherentWrds hyponymPolysem_cnt concretMeaningPOS buildFeatures Overview This package is a collection of functions that aim to enable the user to perform non-trivial tasks in realm of natural language processing.
PowerBI Topic & Sentiment Chart
About the app Installation guide Documentation References Source code License Feedback guidelines Go to about section How to install User Guide References GitHub License Contribute Demo About the app This is a PowerBI application with R script driven visual. This app attempts to help in generating topics and sentiment from text data field by writing low or no code at all.
Build a staff scheduler application
This article provides a perspective for building a simple staff scheduler app as demonstrated here. Note: The author focuses on the reasoning behind the code and therefore detailed code explanation is out of scope. Readers with working knowledge of R language would benefit the most from this article. Table of contents: Motivation Business problem Data Components of building a staff scheduler R code Concluding remarks 1. Motivation From open source algorithms such as glpk to commercial ones such as Gurobi, there are many options available to solve optimization problems.
About the app Installation guide Documentation Frequently Asked Questions Source code License Feedback guidelines Go to about section How to install How-to guide FAQs GitHub License Contribute Demo About the app This application attempts to serve as a template for those who wish to apply integer programming to solve real world problems. The application is built in R Shiny. If there are any questions, please read the FAQ section before leaving comments towards the end of this article.
Picture courtesy: magicalquote 📢This digital library📚 is a collection of opensource software packages, applications and technical articles. Membership is free 🎉 🚀Motivation There are many opensource and commercial sources to gather information about Data science/AI/ML/Cognitive Sciences on the internet. Over the past few years, the author was contacted by many people who were curious to understand about this field. Additionally, the author personally responded to more than 30 questions on Stack Overflow regarding questions from software developers who were working on Data science/AI/ML/Cognitive Sciences.
Synthetic data generator
Note Based on user feedback, this application is now evolved into UnReal. See you there!!
Documentation of R package: Conjurer
This is the official documentation of R package Conjurer. This documentation in its original form is available on CRAN. Following are the badges for this package. 🔔If you are looking for an easy to use GUI for generating synthetic data please check out the app UnReal 🎉 1.Overview 1.1 Background & Motivation Data science applications need data to prototype and demonstrate to potential clients.
Build a customized Entity Recognition model
This article is a how-to guide for training a customized Entity Recognition model. The technical challenges such as installation issues, version conflict issues, operating system issues that are very common to this analysis are out of scope for this article. Table of contents: Business use case for entity recognition Overview of CRF Annotating training data Python Code for deploying CRF References Business use case for entity recognition
Named Entity Recognition
This article is a how-to guide for pythonic implementation of Named Entity Recognition. The technical challenges such as installation issues, version conflict issues, operating system issues that are very common to this analysis are out of scope for this article. Table of contents: What is Named Entity Recognition(NER) Business Use cases for NER Installation pre-requisites Python Code for implementation Additional Reading: CRF model, Multiple models available in the package Disclaimer and Citation What is Named Entity Recognition(NER)?