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GenAIWallah · 100 Days of NLP

Master Natural Language Processing
in 100 Days

Complete 100 Days of NLP curriculum — tokenization, TF-IDF, Word2Vec, text classification, HMMs, and duplicate detection. Free forever.

A structured NLP curriculum — from pipeline foundations and word representations through text classification, sequence labeling, and real-world duplicate detection.

100Days / Lessons
8Core Modules
100+Code Examples
∞Free Forever
Beginner Guide

What is Natural Language Processing?

NLP = Computers ko Hindi, English, aur har bhasha samjhana. AI ka sabse challenging hissa.

📝 Computers Ko Bhasha Sikhao

Natural Language Processing (NLP) is a branch of AI that teaches computers to understand, interpret, and generate human language — text and speech.

Human language is incredibly complex. The same word can have different meanings in different contexts. Consider:

• "Bank" = Financial institution OR side of a river

• "Run" = jogging OR operating a machine OR a tear in stockings

• "I saw her duck" = I saw her pet bird OR I saw her bend down

☕ Chai Wala Analogy: Imagine teaching a foreigner to understand Hindi. "Chai" is simple. But "chai pe charcha" means political discussion. "Chai mein chini kam hai" could mean literal sugar or that something is boring. NLP is teaching computers to understand these nuances — context, sarcasm, culture, and double meanings.

Why NLP is Hard: Language is ambiguous, context-dependent, and constantly evolving. Unlike images (pixels have fixed meanings), words gain meaning from their neighbors and the broader context.

Simple Breakdown

The NLP Pipeline

Har NLP project mein ye 5 steps hote hain. Bas isko yaad kar lo.

Step 1: Text Acquisition — Collect text from websites, PDFs, social media, or databases.
Step 2: Text Cleaning — Remove noise, fix spelling, normalize case, handle emojis.
Step 3: Tokenization — Split text into words, subwords, or characters.
Step 4: Feature Extraction — Convert text to numbers (TF-IDF, Word2Vec, BERT embeddings).
Step 5: Model & Prediction — Train ML/DL models for classification, generation, or translation.
Real Impact

NLP Everywhere

Roz use karte ho, pata bhi nahi chalta.

💬

ChatGPT & Virtual Assistants

Large Language Models use attention mechanisms to generate human-like responses. Trained on billions of text snippets from the internet. The most visible NLP application today.

Learn in Module 5+ →
📧

Email Classification

Naive Bayes and SVM classifiers sort emails into categories (spam, promotions, primary). Gmail processes 300 billion emails daily using NLP.

Learn in Module 6 →
🌐

Google Translate

Sequence-to-sequence models with attention translate between 100+ languages in real-time. Neural Machine Translation (NMT) changed everything.

Learn in Module 7 →
😊

Sentiment Analysis

Companies analyze Twitter, Amazon reviews, and news to understand public opinion. LSTM and BERT models classify text as positive, negative, or neutral.

Learn in Module 6 →
🔍

Search Engines

Google uses NLP to understand your query intent, not just keywords. BERT improved search results by 10% by understanding context and relationships.

Learn in Module 4 →
🏥

Medical Text Analysis

NLP extracts diagnosis, medication, and symptoms from electronic health records. Named Entity Recognition (NER) identifies medical terms automatically.

Learn in Module 7 →
Your Roadmap

Zero to NLP Expert

100 din mein tum computers ko bhasha sikha sakte ho.

Foundational Research

Papers That Built NLP

Ye research ne text ko numbers mein badal diya — aur AI ko bhasha sikha di.

Paper

Word2Vec (Mikolov et al., 2013)

Introduced dense word embeddings where similar words cluster together. "King - Man + Woman ≈ Queen." Revolutionized how computers understand word relationships.

📄 Research•Covered in Module 5
Paper

TF-IDF (Jones, 1972)

Term Frequency-Inverse Document Frequency weighs words by importance in a document vs. the corpus. Still the baseline for information retrieval and search engines.

📄 Research•Covered in Module 4
Paper

BERT (Devlin et al., 2018)

Bidirectional Encoder Representations from Transformers. Reads text in both directions simultaneously for richer context understanding. Powers Google Search.

📄 Research•Connected to DL Module 9
Trending

Named Entity Recognition (NER)

Identifying entities (persons, organizations, locations) in text. Critical for chatbots, search, and knowledge graph construction. Modern NER uses BERT and CRFs.

🔥 Trending•Covered in Module 7
New

LLMs & Prompt Engineering

Large Language Models (GPT-4, Claude) have changed NLP forever. The skill is no longer model training but prompt design — getting the right output through clever instructions.

🚀 Emerging•Connected to LangChain
Paper

Hidden Markov Models (Baum & Petrie, 1966)

HMMs model sequential data with hidden states and observable outputs. Foundation for POS tagging, speech recognition, and bioinformatics sequence analysis.

📄 Research•Covered in Module 7
Curriculum Overview

8 Modules to NLP Mastery

Each module builds on the previous — click any module to dive into detailed notes with code, theory, and exercises.

Module 1
Days 1–12

Introduction to NLP & Ambiguity

Understand what NLP is, the core challenges of natural language, and why ambiguity is the central problem.

  • NLP definition and real-world applications
  • Lexical ambiguity — same word, multiple meanings
  • Syntactic ambiguity — multiple parse trees
  • Semantic and pragmatic resolution strategies
  • Overview of NLP tasks: POS, NER, parsing, MT
Start Module 1 →
Module 2
Days 13–25

End-to-End NLP Pipeline

Trace the full lifecycle of an NLP system from raw data acquisition to model deployment.

  • Data scraping and acquisition strategies
  • Text cleaning and noise removal
  • Embedding computation and representation
  • Model training and evaluation workflow
  • Deployment and serving NLP APIs
Start Module 2 →
Module 3
Days 26–38

Text Preprocessing Techniques

Master all standard text normalization steps before feeding text into any model.

  • Tokenization — word, subword, sentence-level
  • Lowercasing and punctuation handling
  • Stopword removal — when to and when not to
  • Stemming vs. Lemmatization — tradeoffs
  • Regex-based cleaning and custom pipelines
Start Module 3 →
Module 4
Days 39–53

Text Vectorization & TF-IDF

Convert raw text into numerical representations that machine learning models can consume.

  • One-Hot Encoding for vocabulary
  • Bag-of-Words (BoW) representation
  • N-grams and co-occurrence matrices
  • TF-IDF — term and document frequency scaling
  • Sparse vs. dense representation tradeoffs
Start Module 4 →
Module 5
Days 54–65

Word Embeddings (Word2Vec)

Learn dense vector representations that capture semantic meaning and word relationships.

  • Limitations of sparse representations
  • CBOW — predicting target from context
  • Skip-gram — predicting context from target
  • Negative sampling for efficient training
  • GloVe and FastText comparisons
Start Module 5 →
Module 6
Days 66–78

Text Classification Models

Build classifiers that label text — from spam detection to sentiment analysis and topic categorization.

  • Naive Bayes — conditional probabilities & Laplace smoothing
  • Logistic Regression for multi-class text
  • Support Vector Machines with text kernels
  • Evaluation: accuracy, precision, recall, F1
  • Handling class imbalance in NLP
Start Module 6 →
Module 7
Days 79–90

POS Tagging & Hidden Markov Models

Model sequential linguistic structure using probabilistic graphical models and dynamic programming.

  • Part-of-Speech tag sets (Penn Treebank)
  • HMM — states, transitions, emissions
  • Forward-Backward algorithm
  • Viterbi decoding for optimal tag sequence
  • Named Entity Recognition (NER) with HMMs
Start Module 7 →
Module 8
Days 91–100

Duplicate Question Detection

End-to-end NLP case study using the Quora Question Pairs dataset — a real-world similarity problem.

  • Problem framing: semantic similarity as binary classification
  • Cosine similarity and Jaccard intersection
  • Fuzzy matching with edit distance
  • Feature engineering from text pairs
  • XGBoost on engineered similarity vectors
Start Module 8 →
Quick Navigation

Jump to Any Topic

All 100 NLP topics mapped below — click to navigate directly.

What is NLP? NLP Applications Lexical Ambiguity Syntactic Ambiguity Semantic Ambiguity Pragmatic Analysis NLP Task Overview NLP vs CV vs RL Language Models Intro Corpora & Datasets NLTK Setup SpaCy Setup NLP Pipeline Overview Data Scraping Text Acquisition Noise Removal Text Cleaning Embedding Pipeline Model Training Flow NLP API Deployment End-to-End Project Evaluation Metrics Pipeline Debugging Benchmarking Pipeline Project Tokenization Basics Word Tokenization Subword Tokenization Stopword Removal Stemming (Porter) Lemmatization Regex Cleaning Sentence Splitting Custom Pipelines Preprocessing Project One-Hot Encoding Bag of Words N-grams TF-IDF Theory TF-IDF with Sklearn Co-occurrence Matrix Sparse Representations CountVectorizer TF-IDF Search Engine Vectorization Project Dense Embeddings Intro CBOW Architecture Skip-gram Architecture Negative Sampling Word Analogies GloVe Embeddings FastText Embedding Visualization Text Classification Intro Naive Bayes NLP Laplace Smoothing Logistic Regression Text SVM for Text Multiclass Classification Confusion Matrix NLP Precision & Recall Sentiment Analysis Spam Detection Topic Classification Classification Project POS Tagging Intro Penn Treebank Tags HMM Theory HMM Transitions HMM Emissions Forward Algorithm Viterbi Decoding Baum-Welch Training NER with HMMs CRF for Sequence Labeling SpaCy POS Tagger Chunking & Parsing Dependency Parsing Constituency Parsing Sequence Labeling Project Similarity Problem Framing Quora Dataset EDA Cosine Similarity Jaccard Similarity Edit Distance Fuzzy Matching Feature Engineering TF-IDF Features Word2Vec Features XGBoost Classifier Model Evaluation Error Analysis Threshold Tuning Ensemble Approach BERT Embeddings Intro Sentence Transformers Deployment API Scalability Considerations Capstone Project Final Review 🎓

Ready to Start Your NLP Journey?

Begin with Module 1 — no prior NLP knowledge required. All you need is basic Python and curiosity.

Start Day 1 → 100 Days of DL All Generative AI Tutorials →

Frequently Asked Questions

Is this NLP tutorial free?

Yes. GenAIWallah's 100 Days of NLP is completely free — no signup, no paywall. Covers tokenization, TF-IDF, Word2Vec, text classification, HMMs, and a real Quora duplicate detection case study. All in Hindi and English.

What is NLP in simple terms?

NLP (Natural Language Processing) is teaching computers to understand human language. When you ask Siri a question, when Google translates text, when Gmail filters spam — that's NLP. It combines linguistics, computer science, and machine learning to bridge the gap between human communication and computer understanding.

What is the difference between NLP and NLU?

NLP is the broad field — all techniques for processing language (tokenization, translation, text generation). NLU (Natural Language Understanding) is a subset focused on comprehension — what does this text actually mean? When ChatGPT answers your question, it's using NLU. Our NLP course covers both NLP and NLU fundamentals.

What is tokenization and why does it matter?

Tokenization is splitting text into smaller units (tokens) — words, subwords, or characters. It's the first step of every NLP pipeline. "Hello world" becomes ["Hello", "world"]. Different tokenizers handle different languages and special cases. We cover word-level, subword (BPE), and sentence-level tokenization in Module 3.

What is Word2Vec and how does it work?

Word2Vec converts words into dense vectors (numbers) where similar words are close together. It's trained by predicting words from their neighbors (CBOW) or neighbors from a word (Skip-gram). Famous example: King - Man + Woman ≈ Queen. Learn Word2Vec from scratch in Module 5.

What is sentiment analysis and how is it used?

Sentiment analysis classifies text as positive, negative, or neutral. Companies use it to monitor brand reputation, analyze product reviews, and track customer satisfaction. We build a complete sentiment analyzer using Naive Bayes and Logistic Regression in Module 6.

Do I need deep learning knowledge for NLP?

Not to start. This course begins with traditional NLP techniques (TF-IDF, Naive Bayes, HMMs) that require only ML knowledge. Deep learning for NLP (BERT, Transformers) comes after you have the fundamentals. We recommend completing our 100 Days of ML before this NLP track, but it's not strictly required.