Displaying 1 to 10 from 10 results

pytextrank - Python implementation of TextRank for text document NLP parsing and summarization

  •    Jupyter

Python implementation of TextRank, based on the Mihalcea 2004 paper. The results produced by this implementation are intended more for use as feature vectors in machine learning, not as academic paper summaries.

practical-machine-learning-with-python - Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system

  •    Jupyter

"Data is the new oil" is a saying which you must have heard by now along with the huge interest building up around Big Data and Machine Learning in the recent past along with Artificial Intelligence and Deep Learning. Besides this, data scientists have been termed as having "The sexiest job in the 21st Century" which makes it all the more worthwhile to build up some valuable expertise in these areas. Getting started with machine learning in the real world can be overwhelming with the vast amount of resources out there on the web. "Practical Machine Learning with Python" follows a structured and comprehensive three-tiered approach packed with concepts, methodologies, hands-on examples, and code. This book is packed with over 500 pages of useful information which helps its readers master the essential skills needed to recognize and solve complex problems with Machine Learning and Deep Learning by following a data-driven mindset. By using real-world case studies that leverage the popular Python Machine Learning ecosystem, this book is your perfect companion for learning the art and science of Machine Learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute Machine Learning systems and projects successfully.

text-analytics-with-python - Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer

  •    Python

Derive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem. A structured and comprehensive approach is followed in this book so that readers with little or no experience do not find themselves overwhelmed. You will start with the basics of natural language and Python and move on to advanced analytical and machine learning concepts. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems.

Obsei - Low code AI powered automation tool

  •    Python

Obsei is a low code AI powered automation tool. It can be used in various business flows like social listening, AI based alerting, brand image analysis, comparative study and more. It consist of Observer, Analyzer and Informer. Observer observes the platform like Twitter, Facebook, App Stores, Google reviews, Amazon reviews, News, Website etc and feed that information. Analyzer performs text analysis like classification, sentiment, translation, PII on the analyzed data. Informer sends it to ticketing system, data store, dataframe etc for further action and analysis.

quanteda - An R package for the Quantitative Analysis of Textual Data

  •    R

An R package for managing and analyzing text, created by Kenneth Benoit. Supported by the European Research Council grant ERC-2011-StG 283794-QUANTESS. For more details, see https://docs.quanteda.io/index.html.

semantria-sdk - Semantria SDK

  •    CSharp

Semantria is a text analytics and sentiment analysis API. It allows you to gain valuable insights from your unstructured text content by extracting entities, categories, topics, themes, facets, and sentiment. It is based on Lexalytics’ Salience engine. The Semantria SDKs are the most convenient way to integrate with the Semantria API to build a continuous or high-volume application. The SDKs implement all available Semantria features and include some simple examples of their use. However, the examples are not intended to demonstrate the best practices for processing large volumes of data. Please contact Lexalytics for guidance if you plan to build your own application.

node-word2vec - node.js interface to the Google word2vec tool

  •    C

This is a Node.js interface to the word2vec tool developed at Google Research for "efficient implementation of the continuous bag-of-words and skip-gram architectures for computing vector representations of words", which can be used in a variety of NLP tasks. For further information about the word2vec project, consult https://code.google.com/p/word2vec/. Currently, node-word2vec is ONLY supported for Unix operating systems.

text-miner - text mining utilities for node.js

  •    Javascript

The fundamental data type in the text-miner module is the Corpus. An instance of this class wraps a collection of documents and provides several methods to interact with this collection and perform post-processing tasks such as stemming, stopword removal etc. where [] is an array of text documents which form the data of the corpus. The class supports method chaining, such that mutliple methods can be invoked after each other, e.g.

We have large collection of open source products. Follow the tags from Tag Cloud >>

Open source products are scattered around the web. Please provide information about the open source projects you own / you use. Add Projects.