NirantK/NLP_Quickbook
NLP in Python with Deep Learning
This collection of practical guides helps data scientists and machine learning engineers quickly understand and apply Natural Language Processing (NLP) techniques. It takes raw text data and transforms it using methods like cleaning, spell correction, named entity recognition, and text classification. The output is refined text or numerical representations suitable for building real-world applications such as chatbots.
604 stars. No commits in the last 6 months.
Use this if you are a practicing data scientist or machine learning engineer who needs to quickly implement or improve NLP components in your projects.
Not ideal if you are looking for an academic textbook on the theoretical foundations of NLP or are not comfortable with Python code.
Stars
604
Forks
233
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jan 31, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/NirantK/NLP_Quickbook"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related tools
natasha/ipymarkup
NER, syntax markup visualizations
neomatrix369/nlp_profiler
A simple NLP library allows profiling datasets with one or more text columns. When given a...
thepushkarp/nalcos
Search Git commits in natural language
lyeoni/nlp-tutorial
A list of NLP(Natural Language Processing) tutorials
bootphon/pygamma-agreement
Gamma Agreement in Python