Quest 11 of 15
NLP, embeddings & sequence models
Text ML: tokenisation, bag-of-words baselines, word embeddings, RNN/LSTM intuition, and transformer classifiers with Hugging Face.
Start here
This module builds on earlier parts, but every important idea is explained in context. Text ML: tokenisation, bag-of-words baselines, word embeddings, RNN/LSTM intuition, and transformer classifiers with Hugging Face. As you read, connect each concept to the worked example and ask what a person must still decide.
Big idea
Text ML: tokenisation, bag-of-words baselines, word embeddings, RNN/LSTM intuition, and transformer classifiers with Hugging Face.
Learn one idea at a time
Read, explore, then mark each idea when you can explain it.
Idea 1 of 8
Define what one text example and label represent, remove duplicate leakage across splits, and inspect length, language, label balance, and sensitive content before choosing tokenisation or model architecture.
Choose a deep dive
Open the topics you want to explore. The detail stays folded until you need it.
Deep dive 1Baselines matter
A TF-IDF logistic model sometimes beats a huge transformer on 500 rows — always benchmark.
Deep dive 2Small data needs regularisation
Use early stopping, small LR, and class weights for imbalanced support tickets.
Deep dive 3Worked example: NLP, embeddings & sequence models
Transformer overfits: train acc 99%, val acc 62% on 600 examples. A responsible response is: More regularisation, early stopping, data augmentation (back-translation), or collect data. Correct toolkit. Use this case to separate what the technology contributes from what people contribute. The team should compare the intended outcome with a baseline where applicable, record important assumptions, and keep a clear route to correct or stop the process.
Deep dive 4Local check for NLP, embeddings & sequence models
Ask whether the examples, data, and assumptions fit your school, company, or community. Generic demos often miss local names, laws, connectivity, and languages.
Deep dive 5Baseline before transformer
TF-IDF plus logistic regression is fast, inspectable, and sometimes stronger than fine-tuning on a small text dataset.
One-minute challenge
Connect this lesson to real life
Name one situation where this idea could help, and one thing a person should still check.
Explore a real-world example
Use the arrows to connect the idea to a visible situation.
Photo example
Text classification stack
Raw text → tokenizer → encoder → classification head → metrics.
Key terms
Tap a term to flip and read the definition.
Optional further learningFree textbooks and trusted online resources
These sources informed the course structure. Use them to revisit a concept or study it in more depth.
Ready check
Tick each idea only when you could explain it without looking back.
Ready for practice? Fine-tune a DistilBERT model on 1k labelled support messages.
Extra context (audience, logistics, curriculum notes)
Built for: Software developers and technical learners — Python and coding required.
Formats: Learn scroll · Practice interactions · Quiz · Optional chat lab
Module 11 — NLP, embeddings & sequence models
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