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Stop 10 · NLP & Modern AITopics · 4/7

Sequence Models for NLP

Part of the Data Science Roadmap

In short

Applying RNNs/LSTMs (from Phase 9) specifically to text — processing a sequence of word embeddings to capture context and order, the pre-Transformer standard for tasks like sentiment analysis.

How to learn this

  1. 1Build a simple LSTM-based text classifier on a small labeled dataset.
  2. 2Compare its performance to a simpler Bag of Words / TF-IDF baseline model.
  3. 3Learn why sequence order matters for meaning in ways Bag of Words completely ignores.

More topics at this stop

Text Preprocessing (Tokenization, Stemming, Lemmatization)Bag of Words & TF-IDFWord Embeddings (Word2Vec, GloVe)Transformers & Attention (Overview)Pretrained LLMs & Fine-Tuning BasicsPrompt Engineering for Data Tasks

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