02/06/2026
🚀 Learning Natural Language Processing (NLP) From First Principles
Today, instead of jumping straight into NLP libraries and frameworks, I focused on understanding the fundamentals of tokenization manually.
Using a simple text sample, I broke language down into multiple levels:
✅ Sentence Tokens
✅ Subsentence Tokens
✅ Word Tokens
✅ Letter Tokens
✅ Lemmatization
✅ Stemming
✅ Parts of Speech (POS) Tagging
By manually analyzing text, I gained a deeper understanding of how language models process human language before any machine learning or deep learning techniques are applied.
For example:
📌 Text:
"The drums beat and the flutes sang while spectators held their breath."
Can be decomposed into:
Sentences
Clauses/Subsentences
Individual Words
Individual Characters
Lemmas (base forms)
Stems (root forms)
Grammatical Roles (POS)
This exercise revealed how modern AI systems, chatbots, search engines, translators, and Large Language Models (LLMs) transform raw text into structured representations that machines can understand.
As someone with a background in Electrical Engineering and now growing in Software Development, AI, React, JavaScript, and Machine Learning, I believe mastering the foundations is essential before relying on automation tools.
The goal is not just to use AI tools but to understand how they work under the hood.
📚 Current Learning Path:
Natural Language Processing (NLP)
Tokenization Techniques
Linguistic Preprocessing
Machine Learning Fundamentals
Large Language Models (LLMs)
AI Applications
