Every NLP project follows the same fundamental flow. You'll build each stage from scratch before assembling them into production-ready applications.
Raw Text Input
Unstructured text from any source — reviews, tweets, docs, PDFs.
Preprocessing
Clean, normalise, tokenise, remove noise.
Representation
Convert text to numbers a model can use.
Model
Train or fine-tune the right model for the task.
Evaluation
Measure with the right metric for the task.
Deployment
Serve predictions via API endpoint.
You'll understand each era of NLP — not just the latest models. Knowing where each approach fails is what separates engineers who debug well from those who don't.
Bag-of-Words & TF-IDF
Simple frequency-based representations. Still fast, interpretable, and surprisingly effective for short-text classification tasks with small datasets.
✓ Taught in Week 1Word2Vec, GloVe & fastText
Dense vector representations that capture semantic relationships. The first time "king − man + woman ≈ queen" worked mathematically. Introduced contextual meaning to NLP.
✓ Taught in Week 2LSTMs, GRUs & Attention
Recurrent models for sequential text. Attention mechanisms let models focus on relevant parts of input — the crucial precursor to transformers.
✓ Conceptual coverage in Week 3BERT — Bidirectional Transformer
Pre-trained on masked language modelling. Fine-tunable on any downstream task with minimal data. Still the workhorse of production NLP in Indian companies.
✓ Deep dive + fine-tuning in Week 3–4T5, BART, RoBERTa, DistilBERT
Task-specific and efficiency-optimised variants. T5 and BART for generation tasks (summarisation, translation). DistilBERT for faster inference with minimal accuracy loss.
✓ Taught in Week 4–5Sentence Transformers & RAG Foundations
Sentence-level embeddings for semantic search and retrieval. The direct foundation for RAG pipelines covered in the Generative AI course.
✓ Taught in Week 5 (bridge to GenAI course)NLP Engineer
Dedicated NLP roles at conversational AI, legal tech, and fintech companies building text intelligence products.
₹12–24 LPA fresher rangeConversational AI Developer
Build intent classifiers, NER pipelines, and dialogue systems for chatbot and virtual assistant products.
₹10–20 LPA fresher rangeSearch & Relevance Engineer
Semantic search using sentence transformers. E-commerce, legal, and enterprise search are all hot markets.
₹14–26 LPA rangeGenAI Engineer (foundation)
NLP is the prerequisite for GenAI work. This course + the GenAI course = a powerful two-badge combination for LLM roles.
Bridge to Gold + GenAI path// This course is for
- NLP task taxonomy: classification, generation, extraction, retrieval
- Text cleaning: lowercasing, punctuation, HTML stripping, unicode normalisation
- Tokenisation: word, sentence, sub-word (BPE preview)
- Stemming vs lemmatisation — tradeoffs in practice
- Stopwords: when removing them helps (and when it hurts)
- Bag-of-Words and TF-IDF with Scikit-learn
- N-gram features, character-level features
- Project kick-off: spam detection with classical features
- Word2Vec: skip-gram vs CBOW, negative sampling
- Training your own Word2Vec with Gensim on domain data
- GloVe: global co-occurrence matrix approach
- fastText: sub-word embeddings for morphology-rich languages
- Word analogy tasks: king − man + woman ≈ queen
- Visualising embeddings with t-SNE
- Sentence embeddings: averaging, weighted TF-IDF vectors
- Using pretrained embeddings in Scikit-learn pipelines
- Attention mechanism: query, key, value — intuition and maths
- Self-attention and multi-head attention
- Positional encoding: why order matters and how to encode it
- BERT architecture: encoder-only, masked LM, next sentence prediction
- Tokenisation: WordPiece, [CLS], [SEP], [MASK] special tokens
- HuggingFace Transformers: AutoTokenizer, AutoModel, Trainer
- Fine-tuning BERT for sentiment classification on IMDb
- DistilBERT: 40% smaller, 60% faster, 97% of BERT quality
- Named Entity Recognition: spaCy pipeline, custom entity training
- Sequence labelling: BIO tagging, CRF layer
- Text summarisation: extractive (TextRank, LexRank)
- Abstractive summarisation: T5, BART via HuggingFace pipeline
- ROUGE score evaluation for summarisation
- Extractive QA: BERT on SQuAD, span prediction
- Zero-shot classification with BART-MNLI
- Multi-label text classification: sigmoid head, threshold tuning
- Sentence Transformers (SBERT): sentence-level dense embeddings
- Cosine similarity for semantic search and deduplication
- FAISS: fast nearest-neighbour search over embedding stores
- Building a semantic document search engine from scratch
- BERTScore: embedding-based generation evaluation
- Bridge to RAG: retrieval-augmented generation preview
- Model deployment: HuggingFace Spaces + Gradio demo
- Capstone: end-to-end NLP application of your choice
Flipkart Review Sentiment Analyser
Build a three-stage pipeline: TF-IDF → Word2Vec → BERT fine-tuned on 50k Flipkart product reviews. Compare all three approaches and present results.
Legal Document NER System
Train a custom spaCy NER model to extract parties, dates, clauses, and jurisdiction from Indian legal documents. Evaluate with entity-level F1.
News Article Summariser
Combine extractive (TextRank) and abstractive (T5) summarisation. Build a Gradio app that accepts any news URL and returns a one-paragraph summary.
Semantic Job Search Engine
Index 10k+ job descriptions using SBERT embeddings + FAISS. Build a search interface where a query like "ML engineer with PyTorch" surfaces semantically relevant jobs — not just keyword matches.
Newton JEE Gold Badge
NLP & GenAI Specialist — NLP & Text Mining
The First of Two Gold Badges
The Gold tier badge signals NLP & GenAI specialisation — the most in-demand AI skill cluster of 2025. This NLP course earns the first Gold badge; completing the Generative AI & LLMs course earns the second. Together they make a uniquely powerful credential combination on LinkedIn.
The transformer timeline section in week 3 was the clearest explanation of attention I've ever encountered. I had tried to understand BERT from three different sources before this — Arjun explained it in one session and it fully clicked. The fine-tuning project is now my most-viewed GitHub repo.
The legal NER project was unlike anything I'd done before. Building a custom spaCy model for Indian legal documents felt like real work — not a tutorial. An Advocate friend saw the demo and asked if he could use it. That's when I knew this course was the real deal.
I came in knowing Python and basic ML. By week 5 I had a semantic search engine deployed on HuggingFace Spaces. The jump from TF-IDF to BERT to SBERT embeddings was taught in a way that made every transition feel logical, not magical.
Arjun shared a "NLP debugging checklist" in week 4 that saved me hours on the capstone. Things like checking your tokeniser's vocabulary for domain terms, verifying label alignment, and using BERTScore for qualitative evaluation. That checklist is pinned in my notes app permanently.