NLP & Transformers | NetrAInsights

Level

Intermediate

Duration

3 Weeks

Hands-On Labs

Format

Self-paced

What You'll Learn

From tokenization to transformers, this course covers the full NLP stack. Learn to build systems that understand text, translate languages, answer questions, and generate coherent content using state-of-the-art models.

Text Processing: Tokenization, stemming, lemmatization, regex
Classical NLP: TF-IDF, Naive Bayes, word embeddings (Word2Vec, GloVe)
Sequence Models: RNNs, LSTMs, sequence-to-sequence
Transformers: BERT, RoBERTa, T5, GPT for NLP tasks
Named Entity Recognition: spaCy, HuggingFace token classification
Question Answering: Extractive and generative QA systems

Course Modules

💬 Week 1: NLP Foundations▼

Text preprocessing pipeline
Tokenization strategies (word, BPE, SentencePiece)
TF-IDF and bag-of-words
Word embeddings: Word2Vec, GloVe, FastText
Sentiment analysis with classical ML
Lab 1: Build text preprocessing pipeline
Lab 2: Train Word2Vec embeddings
Lab 3: Sentiment classifier with TF-IDF + SVM
Lab 4: Named entity recognition with spaCy

🔄 Week 2: Transformers & Fine-Tuning▼

Transformer architecture deep-dive
BERT: pre-training and fine-tuning
RoBERTa, DistilBERT, ALBERT variants
Hugging Face Trainer API
Multi-class and multi-label classification
Lab 5: Fine-tune BERT for text classification
Lab 6: Token classification (NER) with BERT
Lab 7: Zero-shot and few-shot classification
Lab 8: Semantic search with sentence transformers
Lab 9: Build a document Q&A system

🤖 Week 3: Generation, Translation & Deployment▼

Seq2Seq with T5 and BART
Neural machine translation
Text summarization techniques
GPT-2 for text generation
Deploying NLP models with FastAPI
Lab 10: Text summarization with T5
Lab 11: Language translation fine-tuning
Lab 12: Build a chatbot with GPT-2
Lab 13: Serve NLP model with FastAPI
Lab 14 (Capstone): End-to-end NLP application

Prerequisites

Python programming (intermediate)
Basic ML knowledge (supervised learning)
Familiarity with NumPy and pandas

Who Should Take This?

ML Engineers working with text data
Data Scientists building NLP systems
Backend Developers integrating NLP into products
Researchers exploring language models

Tools & Tech Stack

Python, PyTorch, Hugging Face Transformers
spaCy, NLTK, sentence-transformers
FastAPI, Gradio for deployment
Google Colab, Weights & Biases

Ready to Start?

Build systems that understand and generate human language at scale.

📧 Enroll Now

📝 NLP & Transformers