This is my guide on Natural Language Processing (NLP) structured after following the Udemy course NLP - Natural Language Processing with Python by José Marcial Portilla.
I've done several courses by JM Portilla in the last years and he's a great content creator; his videos are very practical. The intuition of the theoretical concepts is briefly provided and practical coding is explained. I often look for more theoretical insights in papers and text books, but that's not necessary in practice, unless one wants to carry out research.
Note that I would have forked the original repository to add notes, but the material is provided with a download link.
The file NLP_Guide.md provides a general guide of the course and points out to the different notebooks of each section:
- Setup
- Python Text Basics
- NLP Basics
- Part of Speech Tagging & Named Entity Recognition
- Text Classification
- Semantics and Sentiment Analysis
- Topic Modeling
- Deep Learning for NLP
- Text Embeddings
Besides of that:
./utils/contains any utility files, such as the requirements YAML../pics/contains pictures used in the guide../data/contains texts and datasets.
Finally, here is a list of interesting links:
- My repository on notes related to Deep Learning: deep_learning_udacity.
- My repository on text embeddings and some quick applications: tool_guides/sbert.
- NLP Snippets in Python
- Clean and Tokenize Text With Python
- Primer on Cleaning Text Data
Mikel Sagardia, 2022.
Enjoy. No guarantees.