Natural Language Processing
Build text classifiers, summarisers, and conversational AI with modern NLP tools.
About this course
A practical NLP course covering everything from tokenisation and classical text classification through to fine-tuning BERT-family models, retrieval-augmented generation, and building production chatbots. Projects include a sentiment analyser, a document summariser, and a simple RAG pipeline.
Target audience: ML engineers, data scientists, developers building AI-powered text applications
What you will learn
- NLP
- BERT fine-tuning
- RAG
- LLM APIs
- Text classification
Course syllabus
10 modules · video + projects
- 1Text preprocessing: tokenisation, stemming, and lemmatisation
- 2Bag-of-words, TF-IDF, and classical text classification
- 3Word embeddings: Word2Vec, GloVe, and FastText
- 4Sequence models: LSTMs for NLP
- 5The transformer architecture and attention mechanism
- 6BERT, RoBERTa, and fine-tuning for downstream tasks
- 7Named entity recognition and relation extraction
- 8Retrieval-augmented generation (RAG)
- 9Prompt engineering and LLM API integration
- 10Building and deploying a production chatbot
Prerequisites
- –Python
- –Machine Learning Fundamentals
Frequently asked questions
Does this course use the OpenAI API?
Yes, the LLM integration sections use the OpenAI API. A small amount of API credit (under $5) is sufficient for all exercises. The course also covers open-source alternatives using the HuggingFace Hub.
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