Hugging Face – Transformers & NLP Course in Chennai

825 Ratings

Master Natural Language Processing (NLP) and Transformer-based AI models with Edubrights’ Hugging Face – Transformers & NLP training in Chennai.

This course is designed for students, freshers, AI enthusiasts, data scientists, machine learning engineers, software developers, and working professionals who want to build intelligent language-based AI applications.

Gain hands-on experience with Hugging Face Transformers, pre-trained models, NLP pipelines, text classification, sentiment analysis, question answering, model fine-tuning, and real-world AI projects through practical industry use cases.

Key Highlights:

✅ Real-Time NLP & Transformer Projects with Industry Use Cases

✅ Live Instructor-Led Training by AI, NLP & Machine Learning Experts

✅ Hands-On Practice with Hugging Face Transformers Library

✅ Understanding Transformer Architecture & Foundation Models

✅ Working with Pre-Trained Models for NLP Applications

✅ Text Classification, Sentiment Analysis & Language Understanding

✅ Named Entity Recognition (NER) & Information Extraction Techniques

✅ Question Answering, Summarization & Text Generation Workflows

✅ Fine-Tuning Transformer Models on Custom Datasets

✅ Building End-to-End NLP Pipelines & AI Applications

✅ Integration with Python, Deep Learning Frameworks & APIs

✅ Model Evaluation, Optimization & Deployment Strategies

✅ Resume Building, Portfolio Development & Mock Interview Preparation

✅ Career Guidance, Placement Assistance & Certification Support

✅ Flexible Online, Classroom & Weekend Training Options

✅ Corporate Training for AI, NLP & Data Science Teams

Build practical expertise in NLP and Transformers, develop cutting-edge AI solutions using Hugging Face, and become industry-ready for careers in Artificial Intelligence, Machine Learning, and Language Technologies.

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₹30000

₹38000

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Enroll Now View Syllabus

Case Studies and Projects

Hours of Training

20+

Placement Assurance

100%

Expert Support

Yes

Support & Access

Lifetime

Certification

Yes

Skill Level

All

Language

All

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.