Khirod's Tech Blog

Parsing resume and to extract data from the resume is really a tough work for the recruiter or whoever want to extract some useful information from the text document here in this blog, Basically, we are going to more focus on the summarization of resume. Resumes are a great example of unstructured data and it has its own style and format to preprocess data it will take a really good amount of time its obvious . and also this is really tough task to extract information from this type of document for a human. so now our task is to build a model that will extract useful information automatically. Automate resume parsing using Spacy and NLTK Spacy is an opensource powerful python package in NLP for doing various NLP tasks. This library is published by Matthew Honnibal and Ines Monstani under MIT license. So, what are the features of it? Spacy is the one-stop for usual task in NLP project. The features are: - Tokenization Lemmatization Part-of-speech tagging Entity reco

  Audio is a very complex item to understand for a machine and nowadays audio-related activity in AI is highly used in the market so we can say it can be really a useful and knowledge full article. As we are data scientists, we know feature extraction is a significant activity in almost all machine learning activities. So in this article, we will talk about this feature extraction techniques audio/speech Speech contains frequency from 0 to 5khz if The objective of automatic speaker recognition is to extract, characterize and recognize the information about speaker identity. --> Speech recognition system contains two main modules: Feature Matching Feature Extraction FEATURE MATCHING It is the procedure to identify the unknown speaker by comparing extracted features from his/her voice input with the ones from a set of known speakers. Now let us see about feature extraction in a precise manner. FEATURE EXTRACTION Extract a small amount of data from the voice signal that can be us