Module 2 · Natural Language Processing (NLP)

Text Classification: lessons

Sentiment analysis, topic detection and ticket routing — from TF-IDF + logistic regression to fine-tuned transformers.

4 lessons · 1h 8m

What this module teaches, what it asks of you first and how you practise it are on its syllabus page.

Text Preprocessing Pipelines
14 min
Every preprocessing step destroys information. This lesson is about deciding deliberately what to merge, what to keep, and why the answer differs for TF-IDF and for transformers.
Included with the track
Classic Text Classifiers
18 min
TF-IDF with logistic regression: trains in a second, its weights are readable, and it is often enough. This lesson builds one from 60 reviews all the way to a routing threshold.
Included with the track Simulation inside
Fine-Tuning Transformers for Classification
20 min
Borrow a model that has already read millions of Indonesian sentences, attach a 2,307-parameter classification head, train briefly — then decide honestly whether the result is worth the cost.
Included with the track Simulation inside
Classification with LLMs
16 min
One prompt can classify without a single training label. The question is no longer "can it", but when it is cheaper, when it costs more, and how you know it is right.
Included with the track Simulation inside

Text Preprocessing Pipelines