NLP Engineering

NLP Engineering Series

Welcome to the NLP Engineering Series! This series teaches Natural Language Processing from first principles, with every concept worked through on a concrete text example before touching code.

Sections

1. Introduction — Roadmap and practical use cases for NLP
2. Text Preprocessing — Tokenisation, stemming, lemmatization, stopwords, POS tagging, NER
3. Text Representation — One-Hot Encoding, Bag of Words, N-Grams, TF-IDF
4. Word Embeddings — Word2Vec intuition, CBOW, SkipGram, AvgWord2Vec, Gensim in practice
5. Projects — Spam classification, best practices, and a full sentiment analysis capstone

Prerequisites

• Comfortable with Python (lists, dicts, loops, string methods)
• Basic ML concepts: features, labels, train/test split, accuracy
• No deep learning required until the very end of the series

How to Use This Series

• Follow the sections in order — each one builds directly on the last
• Every post traces its calculations by hand before showing the code
• Run the code examples yourself on the anchor text used in each post

Start your NLP journey!