NLP with Transformers (Hugging Face) Training

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NLP with Transformers (Hugging Face) Training — brochure

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About the course

Why take NLP with Transformers (Hugging Face) Training?

NLP with Transformers (Hugging Face) is a hands-on course for practitioners who want to go beyond calling an API and actually adapt transformer models to their own data. You learn how tokenisation, attention and pretrained checkpoints fit together, then use the Hugging Face stack to fine-tune models for tasks such as classification, token tagging, extractive question answering, summarisation and embedding-based retrieval.

A large part of the course is about doing this efficiently and responsibly. You use LoRA and other parameter-efficient methods to fine-tune on a free GPU, build proper train, validation and test splits, choose metrics that match the task, and run error analysis. You also compare a fine-tuned small model against a prompted LLM so you can justify the choice on accuracy, cost, latency and privacy.

There is no external exam. The course ends with a capstone and an Assure Success course-completion certificate.

Who should attend

● Data scientists and ML engineers working on text problems
● Developers who completed NLP with Python and want to go deeper
● AI engineers building search, extraction or classification systems
● Researchers and graduate students who want a practical transformer workflow
● Teams with domain text (legal, finance, healthcare, support) that generic models handle poorly

Prerequisites

● Solid Python and basic machine learning knowledge
● Familiarity with NLP basics (tokens, embeddings, classification); NLP with Python is a good preparation
● Basic idea of neural networks and a deep learning framework (PyTorch preferred) is helpful

What this certification does for you

Practical, not just API calls

You learn to adapt models to your data rather than depending only on prompts.

Efficient on a budget

LoRA and quantisation techniques make this feasible on free GPU notebooks.

Decision framework

A clear way to choose between a fine-tuned small model and a larger prompted model.

Reusable skills

The Hugging Face workflow applies to vision and audio models too.

Curriculum

10 modules, step by step

MODULE 1

Transformer Foundations

Understand how transformers process language.

› Self-attention and positional information
› Encoder, decoder and encoder-decoder families
› Pretraining vs fine-tuning
› Hugging Face Hub and model cards

MODULE 2

Tokenisers and Datasets

Prepare text data correctly.

› BPE, WordPiece, SentencePiece
› Hugging Face Datasets
› Padding, truncation and batching
› Splits and label quality

MODULE 3

Fine-Tuning for Classification

Adapt an encoder to a custom task.

› Trainer API and training arguments
› Sentiment and intent classification
› Metrics and early stopping
› Handling class imbalance

MODULE 4

Token Classification and Question Answering

Tackle span-level tasks.

› NER label alignment
› Extractive QA on a custom corpus
› Exact match and F1
› Long documents and chunking

MODULE 5

Generation and Summarisation

Work with sequence-to-sequence and decoder models.

› Decoding strategies: greedy, beam, sampling
› Summarisation fine-tuning
› ROUGE and its limits
› Human evaluation basics

MODULE 6

Parameter-Efficient Fine-Tuning

Fine-tune larger models on limited hardware.

› LoRA and PEFT
› Quantised training concepts
› Mixed precision and gradient accumulation
› Merging and saving adapters

MODULE 7

Embeddings and Semantic Search

Build retrieval on your own text.

› Sentence embeddings
› Vector indexes
› Bi-encoder vs cross-encoder re-ranking
› Evaluating retrieval (recall@k, MRR)

MODULE 8

Evaluation, Safety and Comparison with LLM Prompting

Choose the right solution.

› Error analysis and slicing
› Bias and robustness checks
› Fine-tuned model vs prompted LLM: accuracy, cost, latency, privacy
› Data privacy considerations for Indian enterprises

MODULE 9

Deployment

Serve a model.

› Inference pipelines and batching
› ONNX export and quantisation overview
› Serving with FastAPI
› Monitoring input drift

MODULE 10

Capstone Project

Deliver a fine-tuned NLP system.

› Select a domain task and dataset
› Fine-tune, evaluate and compare
› Document findings and limits
› Presentation and feedback

What you get

Why take NLP with Transformers (Hugging Face) Training?

Who should attend

Prerequisites

What this certification does for you

10 modules, step by step

Certified by Assure Success

Your path to certification

Skills you will gain

Where this course can take you

Why learn with Assure Success