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Natural Language Processing (NLP)

Text Classification

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

Level
Intermediate
Lessons
4
Total time
1h 8m
Simulations
3
Included in Natural Language Processing (NLP) · $9
Tools you'll use
  • Python
  • PyTorch
Skills
  • Sentiment analysis
  • Fine-tuning
  • Hugging Face

What you will be able to do

  • Build a strong text classification baseline
  • Fine-tune a transformer on your own labels

Before you start

A suggestion, never a lock — you can start here.

  • text-representation
Modules that help first

Syllabus

4 lessons · 1h 8m

Every lesson's title and summary is public. The lessons themselves open once you own the track.

Every lesson in Natural Language Processing (NLP)

  1. 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

  2. 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

  3. 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

  4. 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
Lifetime access
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