A comprehensive deep dive into Natural Language Processing and Transformer models. From attention mechanisms to BERT and GPT, build production NLP systems for classification, NER, QA, and summarisation.
Robust text processing pipelines. Tokenisation strategies (BPE, WordPiece, SentencePiece), normalisation, handling multilingual text, and building preprocessing pipelines that work at scale.
From Word2Vec to contextual embeddings. Understand vector spaces, similarity measures, embedding visualisation, and how modern embedding models capture semantic meaning.
Master attention from the ground up. Scaled dot-product attention, multi-head attention, cross-attention, and why attention is the key innovation that powers modern NLP.
Understanding BERT and its family: RoBERTa, ALBERT, DeBERTa, and distilled variants. Pre-training objectives, fine-tuning for downstream tasks, and choosing the right model size.
Decoder-only Transformers in depth. Autoregressive generation, causal attention masks, temperature and sampling strategies, and understanding the design choices behind GPT-4 and similar models.
Build production classifiers and NER systems. Multi-label classification, few-shot classification, custom entity recognition, and handling noisy real-world text data.
Advanced sentiment analysis beyond positive/negative. Aspect-based sentiment, extractive and generative QA systems, and building reliable text understanding pipelines.
Extractive and abstractive summarisation. PEGASUS, BART, and LLM-based summarisation. Evaluation metrics (ROUGE, BERTScore), handling long documents, and building summarisation APIs.
ML engineers who want to specialise in NLP and build production text processing systems
Software engineers building products that need text classification, extraction, or generation
Data scientists working with text data who want to go beyond basic bag-of-words approaches
Python and basic deep learning knowledge required. You should be comfortable with PyTorch, understand neural network training, and have some exposure to Transformer concepts. Our Deep Learning Masterclass provides ideal preparation.
Interactive sessions with real-time Q&A and screen sharing
All sessions recorded and available for 12 months after the course
Real-world projects that build your portfolio as you learn
Personal mentoring sessions to address your specific questions
Industry-recognised certificate upon successful completion
Our instructors are seasoned practitioners with years of experience building production AI systems. They hold certifications across major cloud platforms and have trained thousands of professionals worldwide.
This course covers the full NLP landscape -- embeddings, BERT, classification, NER, QA, and summarisation. The LLM Fine-Tuning course focuses specifically on customising large generative models. Many students take both for complete NLP mastery.
Absolutely. LLMs are powerful but expensive and slow for many tasks. Specialised NLP models for classification, NER, and extraction are faster, cheaper, and often more accurate for specific tasks. Understanding the full NLP toolkit makes you a more effective engineer.
We primarily work with English but cover multilingual models (XLM-R, mBERT) and techniques for low-resource languages. The principles apply to any language.
Basic NLP exposure is helpful but not required if you have deep learning experience. We build from foundations (tokenisation, embeddings) up to advanced architectures. The Deep Learning Masterclass is sufficient preparation.