Course Objectives
1. What will you learn in the Hugging Face – Transformers & NLP course?
You will learn how to build modern Natural Language Processing (NLP) and Generative AI applications using the Hugging Face ecosystem. The course covers Transformers, pre-trained language models, tokenizers, pipelines, fine-tuning, text classification, sentiment analysis, question answering, text generation, embeddings, model deployment, and MLOps best practices.
2. How does this course prepare you for real-world AI and NLP projects?
The course provides hands-on experience in building AI-powered chatbots, document analysis systems, sentiment analysis solutions, language translation, text summarization, recommendation engines, and enterprise NLP applications using Hugging Face Transformers.
3. What practical skills will you develop during the training?
You will gain expertise in Hugging Face Transformers, tokenizers, datasets, pipelines, BERT, RoBERTa, GPT models, T5, embeddings, model fine-tuning, inference optimization, Python integration, PyTorch, TensorFlow, and model deployment.
4. How will real-time projects improve your Hugging Face skills?
You will work on practical projects involving chatbot development, document summarization, sentiment analysis, AI search engines, question-answering systems, custom language models, and enterprise NLP solutions.
5. What career opportunities can you pursue after learning Hugging Face – Transformers & NLP?
This course prepares you for roles such as NLP Engineer, AI Engineer, Machine Learning Engineer, Generative AI Engineer, Data Scientist, LLM Engineer, AI Application Developer, and Research Engineer.
Popular Techniques Covered in This Course
Introduction to NLP and Hugging Face
Transformers Architecture and Pre-trained Models
Tokenizers and Hugging Face Pipelines
BERT, GPT, T5, RoBERTa, and DistilBERT Models
Text Classification and Sentiment Analysis
Named Entity Recognition (NER) and Question Answering
Text Summarization and Machine Translation
Model Fine-Tuning with Hugging Face Datasets
Model Deployment and API Integration
Enterprise NLP and Generative AI Best Practices
Get Hands-on Knowledge about Real-Time Projects
Project 1
Project 1: AI Customer Support Chatbot
Build an intelligent chatbot using Hugging Face Transformers capable of answering customer queries using pre-trained language models.
Project 2
Project 2: Sentiment Analysis Platform
Develop an NLP solution that analyzes customer reviews, social media posts, and feedback to identify positive, negative, and neutral sentiments.
Project 3
Project 3: Intelligent Document Summarization
Create an AI application that automatically summarizes lengthy documents, reports, legal contracts, and research papers.
Project 4
Project 4: Resume Screening System
Develop an NLP-based recruitment system that extracts candidate skills, analyzes resumes, and matches applicants with job descriptions.
Project 5
Project 5: Enterprise Question Answering System
Build an AI-powered search and question-answering application using Hugging Face Transformers to retrieve information from enterprise documents.
Key Features
Edubrights offers HUGGING FACE – TRANSFORMERS & NLP Training in virtual mode with expert trainers. Here are the key features,
40 Hours Course Duration
100% Job Oriented Training
Industry Expert Faculties
Free Demo Class Available
Completed 500+ Batches
Certification Guidance
Curriculum
Module 1: Introduction to Hugging Face Ecosystem
- Overview of Hugging Face: Model Hub, Datasets, Spaces, and Inference API
- Installing and configuring the Transformers library
- Navigating the Hugging Face Model Hub
- Understanding model cards and selecting the right model
Module 2: Transformer Architecture Fundamentals
- Attention mechanism and self-attention explained
- Encoder-only, decoder-only, and encoder-decoder architectures
- Popular models: BERT, GPT-2, T5, RoBERTa, and DistilBERT
- Tokenisation: BPE, WordPiece, and SentencePiece
Module 3: NLP Pipelines with Transformers
- Using the Hugging Face pipeline API for NLP tasks
- Text classification and sentiment analysis
- Named entity recognition and token classification
- Question answering, summarisation, and translation
Module 4: Fine-Tuning Pre-Trained Models
- When and why to fine-tune vs use out-of-the-box models
- Preparing datasets with the Hugging Face Datasets library
- Fine-tuning BERT for text classification with Trainer API
- Evaluating fine-tuned models with standard NLP metrics
Module 5: Parameter-Efficient Fine-Tuning (PEFT)
- Introduction to PEFT: LoRA, QLoRA, and Prefix Tuning
- Fine-tuning large models on limited hardware
- Using the PEFT library with Hugging Face Transformers
- Practical fine-tuning of Llama and Mistral models
Module 6: Working with Large Language Models
- Loading and running LLMs locally with Transformers
- Quantisation with bitsandbytes for efficient inference
- Text generation parameters: temperature, top-p, and repetition penalty
- Deploying models with Hugging Face Inference Endpoints
Module 7: Hugging Face Spaces and Deployment
- Building and deploying NLP apps with Gradio on Spaces
- Hugging Face Inference API for production use
- Model optimisation with ONNX and TorchScript
- Cost and latency considerations for production NLP
Module 8: Hands-on Projects and Assessment
- Fine-tuning a sentiment classifier for a domain-specific dataset
- Building an NLP pipeline application deployed on HF Spaces
- End-to-end NLP project with evaluation and reporting
- Final assessment and course certification
Receive Training From Our Skilled and Effective Trainers
Experience in the Industry Learn from NLP engineers who have fine-tuned transformer models and deployed Hugging Face pipelines for text classification, summarisation, and named entity recognition at scale.
Backgrounds at the Top Our Hugging Face trainers have contributed to NLP projects at leading AI research organisations and enterprise data science teams.
Clear & Effective Teaching Transformer architecture, tokenisation, model fine-tuning, and the Hugging Face ecosystem are taught progressively from fundamentals to deployment.
Hands-On Learning Focus Students fine-tune pre-trained models, build NLP pipelines, and deploy Hugging Face models using Inference API and Hugging Face Spaces.
Up-to-Date Knowledge Trainers cover the latest Hugging Face releases including Transformers, Datasets, PEFT, and the Model Hub with thousands of pre-trained models.
Professional Hugging Face – Transformers & NLP Certification
The Professional Hugging Face – Transformers & NLP Certification validates your expertise in building AI-powered Natural Language Processing applications using the Hugging Face ecosystem.
Course FAQs
1. What is Hugging Face?
Answer: Hugging Face is one of the world's leading open-source AI platforms that provides pre-trained Transformer models, NLP libraries, datasets, and tools for building Generative AI and Natural Language Processing applications.
2. What are Transformers?
Answer: Transformers are deep learning models that process and understand human language efficiently using attention mechanisms, powering modern AI applications such as ChatGPT, BERT, and T5.
3. What is Hugging Face Transformers?
Answer: Hugging Face Transformers is a Python library that provides thousands of pre-trained models for NLP, computer vision, speech recognition, and Generative AI applications.
4. Which programming language is used with Hugging Face?
Answer: Python is the primary programming language used with Hugging Face, along with PyTorch, TensorFlow, and JAX.
5. Can Hugging Face models be fine-tuned?
Answer: Yes. Hugging Face provides tools to fine-tune pre-trained models using custom datasets for domain-specific AI applications.
6. Which industries use Hugging Face?
Answer: Healthcare, banking, finance, legal, e-commerce, education, software development, telecommunications, media, research, and AI startups use Hugging Face technologies.
7. Is Hugging Face suitable for enterprise AI development?
Answer: Yes. Hugging Face is widely used for enterprise AI applications, including chatbots, document processing, recommendation systems, and Generative AI solutions.
8. Does the course include practical projects?
Answer: Yes. The course includes hands-on projects involving AI chatbots, NLP automation, text summarization, sentiment analysis, and enterprise AI solutions.
9. Is Hugging Face beginner-friendly?
Answer: Yes. Basic knowledge of Python, machine learning, and deep learning is recommended. The course starts with NLP fundamentals before progressing to advanced Transformer models.
10. Why should professionals learn Hugging Face?
Answer: Hugging Face is one of the most widely adopted AI platforms for NLP and Generative AI, making it a valuable skill for AI engineers, data scientists, and machine learning professionals.
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