NLP to LLM from Basic Principles: NLPlanet NLP and ML pt.1

Photo by Joshua Reddekopp on Unsplash

Preface:

I am undertaking a Natural Language Processing and Machine Learning course that uses NLTK and HuggingFace. I have taken notes on their lessons and would like to share them. Although I tried to be in-depth and transformative with my notebook, I strongly encourage following their course as it is detailed and well-formatted.

Credit:

Course Link:

Welcome to the Course — Practical NLP with Python (nlplanet.org)

(Section link provided further down)

Author:

Fabio Chiusano — Medium

Note Information:

This is a Jupyter Notebook uploaded to my Github as provided in the link further down.

Notes include definitions, examples, and explanations.

Uses Pandas and scikit-learn.

Title:

Section 1.4

Statistical Approaches and Text Classification with N-grams.

Section Link:

1.4 Statistical Approaches and Text Classification with N-grams — Practical NLP with Python (nlplanet.org)

Information Covered (All Defined in Notes):

• Difference between Expert Systems and Statistical Approaches.
• Text Classification with N-grams including:
• Vectorization
• N-Grams
• Bag of Words
• Sparse Matrix
• Dense Matrix
• Making a logistic regression model.
• Model training with feature weights (unigrams and bigrams)

Note Link:

NLPlanet Lesson 1.4 Notes: Statisctial approaches and text classification with N-grams.