NLP with Transformers

Tokenisation, attention, pretraining and fine-tuning for classification, extraction and sequence tasks with Hugging Face.

⏱ 8 weeks🎚 Intermediate🧩 3 projects · Certificate🖥 Any device · our GPUs

Apply for this course Where this leads

Draft syllabus. Timeline and topics are indicative and being finalised with the teaching team — the shape is right, the week-by-week detail may shift.

🎯 Who it's for

Engineers with deep-learning basics who want to work on language.

✅ Prerequisites

Deep Learning Foundations with PyTorch or equivalent
Python fluency
Comfort with the training loop

Topics covered

Tokenisation and embeddings
The transformer, in depth
Fine-tuning for classification and NER

Sequence-to-sequence tasks
Evaluation for NLP
Serving and latency

Expected completion timeline

8 weeks part-time at 8–12 hours per week ≈ 1.8 months. Self-paced learners can go faster; the live cohort keeps this pace.

Wk 1–3

Transformers and fine-tuning

You produce: A fine-tuned text classifier.

Wk 4–6

Extraction and seq2seq

You produce: An information-extraction or summarisation model.

Wk 7–8

Evaluation, serving and project

You produce: A served model and an evaluation report.

🧭 Where this fits

Core of the Computer Vision / NLP Engineer path. See the career & salary map for the full path, the expected pay by region, and the total cost.

💳 Delivery & fees

Available in all four delivery formats. Fees vary by format and are confirmed on your advisor call; instalments available. [Add the fee and the next cohort date here.]

Add this to your plan

Book a call and we'll place this course in a full path toward the role you want.

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