Day 04 — Part 2: NLP Basics¶
Text is the most abundant data type in the world. Product reviews, support tickets, social media posts, medical notes — all of it is text, and almost none of it comes pre-labelled, pre-structured, or pre-cleaned. This session covers the full pipeline from raw string to trained classifier, and gives you the intuition for when to reach for classical methods versus a transformer.
Session Overview
Difficulty: Intermediate Reading time: ~2.5 hours | Exercises: ~1.5 hours Prerequisites: Python string operations and regex · Pandas text handling · Classification algorithms (logistic regression) · Scikit-learn Pipeline
Learning Objectives¶
By the end of this session you will be able to:
- Explain why language understanding is fundamentally hard for machines
- Build a text preprocessing pipeline that removes noise without destroying signal
- Convert raw text into numeric feature matrices using Bag of Words and TF-IDF
- Train and evaluate a sentiment classifier using scikit-learn pipelines
- Describe the transformer architecture at an intuition level and use HuggingFace's
pipeline()in five lines - Make an informed choice between TF-IDF + classical ML and a fine-tuned transformer
Schedule¶
| # | Topic | File | Estimated Time |
|---|---|---|---|
| 1 | NLP Overview | 01-nlp-overview.md |
20 min |
| 2 | Text Preprocessing | 02-text-preprocessing.md |
25 min |
| 3 | Bag of Words and TF-IDF | 03-bow-tfidf.md |
30 min |
| 4 | Sentiment Classification | 04-sentiment-classification.md |
35 min |
| 5 | Transformers Overview | 05-transformers-overview.md |
25 min |
| 6 | Exercises | 06-exercises.md |
45 min |
Total reading + coding time: ~3 hours
Difficulty: Intermediate — assumes you are comfortable with scikit-learn's fit/transform pattern and pandas DataFrames.
Prerequisites: Feature Engineering (Day 03), scikit-learn classification basics (Day 02 Part 2)
Tip
Work through the code examples yourself. Copy each block, run it, then break it deliberately — change a parameter, swap a method, feed it different text. That is how preprocessing intuition is built.