• This repository will house all things related to NLP and what you need to know to be instrumental.

• This folder will document and house the more important functionalities of NLP on how to:
  • Preprocess your Text
  • Functions that will help you preprocess
  • Customization of functions to get what you need
  • YouTube Video can be found here

I cover what an NER is and how to use this model! If you are interested in labeling documents without reading them, this might be the repository for you!

• Text classification and categorization
• Utilizes prebuilt models
• Depends on SpaCy
• YouTube video can be found here

I cover sentiment analysis at a very high level, covering all the basics to get you started on your NLP journey! The twitter data can be retrieved here

• Sentiment Analysis Youtube Video
• Preprocessing: Tokenization, Regex, Lemmatization, Stop Words
• Word2Vec
• Embedding Layer
• LSTM Classification

I cover the high level overview on the transformer architecture, covering all the logic and basics that go into the development of this state-of-the-art model. This model is the successor to popular models such as RNN, GRU, and LSTM's since transformers are easily parallelizeable and have greater understanding with context.

• YouTube Video can be found here

I cover the high level overview on the architecture of BERT, building off of the transformer architecture, where I cover all the logic and basics that go into the development of such a model. In addition, I evaluate the pros and cons of BERT and LSTM, AND provide a code implementation with a sentiment analysis prompt.

• YouTube Video can be found here

I cover the high level overview on the architecture of QA Models (based on BERT). I also go into depth on what QA Modeling is, how it can be applied, and how it is used in the real world. I also cover the pretraining and fine-tuning phases of the QA Modeling process.

• YouTube Video can be found here