Natural Language Processing (NLP) is a machine learning focused on analyzing, understanding and generating texts. The works of NLP is a very suffiscated task but can have simple roots in certain frameworks designed to minimize the works necessary to get a NLP machine learning tasks up and running. Notable python library like SpaCy, NLTK and others have amazing functionalities to training,performing and running simple NLP taks.

NLTK Processing Tools is a quick project that runs through the basic provisions of the python library NLTK, for working and processing texts. This library just as others provide tokenizations, stemmer and lemmatization capabilities.

Just like NLTK SpaCy NLP is all about SpaCy Library and the easiness of using its functionarities in processing and training NLP models. SpaCy is a very nice library that provides a ton of functionalities, enabling utmost flexibity to counter the complexities that come from working with text datas. SpaCy functions like Match, Entity_Ruler, Pipelines, Custom Components, Regex patterning etc are little about the features that make SpaCy a great library for NLP tasks

The Basic NLP project outlines the common methods and processes of training an NLP model based on texts and reviews about a book while using classical scikit-learn regresson technique.

SpaCy Stock Analysis is a project that utilizes the full capabilities of SpaCy functions to solve an Investor problem; Identifying Stocks, Companies and Indexes from News Article. The Project showcases the possibilities and easiness that lie with SpaCy functions in applying them to business problems