- 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