Transformer Models and Language Understanding Systems

Unlock advanced language understanding with transformers

Modern NLP systems rely on deep learning architectures that can capture context, relationships, and meaning across complex language.

On this advanced course, you’ll move beyond classical text models and explore the technologies behind today’s language understanding systems.

Begin with sequence modelling using recurrent neural networks, LSTMs, and GRUs, examining how these approaches process text and where their limitations emerge.

You’ll then explore the attention mechanism and why it transformed the way models handle long-range dependencies in language.

Decode the transformer architecture

Explore self-attention, multi-head attention, positional encoding, and encoder-decoder structures to understand how transformer models process language at scale.

You’ll connect these concepts to the shift from recurrence towards modern transformer-based NLP.

Adapt pretrained models such as BERT

Discover how transfer learning and fine-tuning allow pretrained transformer models to tackle specialised language tasks.

You’ll work with models such as BERT and examine how existing language representations can be adapted to new datasets and use cases.

Tackle advanced language understanding tasks

Apply modern NLP techniques to named entity recognition, question answering, and text summarisation.

You’ll also consider model evaluation and responsible NLP practices when creating real-world language systems.

By the end, you’ll understand how deep learning and transformer architectures support advanced NLP applications and be equipped to create and evaluate end-to-end language understanding systems.

This course is for NLP engineers, data scientists, machine learning engineers, and AI developers ready to progress into deep learning and transformer-based NLP. Experience with text preprocessing, classification, evaluation, and word embeddings is recommended.

No prior experience with transformer models or machine translation is required, although a basic familiarity with Python and core NLP concepts will help you follow the demonstrations. The course introduces the tools and setup you need from the beginning, including Hugging Face, the Python libraries, pretrained models and datasets used in the practical activities. You will need access to a suitable computer, a stable internet connection and the relevant online accounts to follow the hands-on exercises.

This course is for NLP engineers, data scientists, machine learning engineers, and AI developers ready to progress into deep learning and transformer-based NLP. Experience with text preprocessing, classification, evaluation, and word embeddings is recommended.

• Explain how neural architectures, attention mechanisms, and transformers represent and process language.
• Apply fine-tuning and transfer learning to adapt pretrained models such as BERT and GPT for NLP tasks.
• Develop neural and transformer-based machine translation workflows and evaluate them using standard metrics.
• Identify how speech, text-to-speech, and vision-language models extend NLP across multiple modalities.
• Design efficient, explainable, and context-aware conversational systems that support responsible NLP development.