Natural Language Processing
Working with text: tokens, embeddings and transformers.
Natural Language Processing is taught here in 3 lessons, running from Text preprocessing and tokens through to Transformers and fine-tuning basics. Each lesson takes one topic, shows the working code, and links onward to the next, so the course can be read straight through in order.
| Track | AI & Intelligent Development |
| Lessons | 3 |
| Level | Beginner |
| Reading time | about 39 minutes |
| Prerequisites | Helpful, but not required: PyTorch |
Lessons
- Text preprocessing and tokensTurn raw text into clean, tokenised input: normalisation, a sparse baseline, and how subword tokenizers actually split words.
- Word and sentence embeddingsWhy dense vectors replaced sparse counts, how cosine similarity works, and how to use sentence embeddings for real retrieval.
- Transformers and fine-tuning basicsWhat self-attention computes, how encoder and decoder stacks differ, and when fine-tuning is worth the cost compared with prompting.
More in AI & Intelligent Development
AI Basics AI Agents Math for AI Machine Learning scikit-learn TensorFlow PyTorch LangChain Ollama OpenCV Codex Claude Code OpenCode Vibe Coding Selenium Playwright
FAQ
How long does the Natural Language Processing course take?
It has 3 lessons, about 39 minutes of reading. Expect roughly twice that if you type out and run every example.
Do I need prior experience for Natural Language Processing?
Not strictly. It helps to have read PyTorch first, because some lessons build on it, but every lesson explains its own assumptions.
What should I read after Natural Language Processing?
Continue with LangChain (3 lessons), the next course in AI & Intelligent Development.
Last refreshed 2026-09-18.