Master Nlp With Nltk In Python

Master NLP fundamentals by building real projects using NLTK — tokenize, extract, generate, and analyze text with Python

What you'll learn

Understand the core principles of Natural Language Processing (NLP) and how text data is processed, cleaned, and analyzed using Python.

Master the NLTK library to perform tasks such as tokenization, POS tagging, chunking, named entity recognition, and syntactic analysis.

Build hands-on NLP applications such as a Shakespeare-style text generator, resume skill extractor, and synonym-based sentence transformer using only NLTK.

Analyze real-world text datasets by working with corpora, computing word frequencies, exploring author styles, and designing autocomplete-like features.

Learn to extract structured information like names, dates, and entities using chunking, regular expressions, and grammar-based pattern matching.

Requirements

Basic knowledge of Python: You should be comfortable with variables, functions, loops, and basic data types (lists, strings, dictionaries).

No prior NLP experience required: We’ll start from scratch and explain everything clearly with hands-on demos.

A computer with internet access: You’ll need to install Python and a few packages (Anaconda is recommended, and we'll guide you step-by-step).

Curiosity to work with real-world text data: Whether you're a student, developer, or researcher, all you need is a willingness to learn by doing.

Description

This is one of the most hands-on and comprehensive courses ever built for Natural Language Processing (NLP) using the NLTK library in Python.Whether you're a student, developer, or researcher, this course will guide you step-by-step from the absolute basics of NLP to building your own mini projects like a Shakespeare-style text generator, resume parser, and synonym-based sentence rewriter — all using just Python and NLTK.You won’t just learn the theory — you’ll apply it. Each section comes with real code walkthroughs, quizzes to test your understanding, and mini projects that you can proudly showcase in your portfolio.What You’ll Learn:Tokenize and clean text data using NLTK’s powerful utilitiesExplore and analyze large corpora like Gutenberg, Brown, and ReutersBuild your own autocomplete-like tool using n-gram language modelsExtract named entities like people, locations, and organizations from raw textParse sentences using syntax trees and context-free grammarUse regular expressions for information extraction (emails, dates, names)Understand word meanings, synonyms, and relationships with WordNetGenerate creative sentences and evaluate language modelsWrite Python scripts that classify text, extract insights, and transform languageProjects You'll Build:Author Style Analyzer (from corpus data)Resume Skill Extractor (from unstructured text)Shakespeare-Style Text Generator (using trigrams)Autocomplete Suggestion Engine (with n-grams)Synonym Sentence Swapper (using WordNet)This course is purely focused on NLTK — it won’t cover modern neural network models or transformer libraries like spaCy, BERT, or HuggingFace. The goal is to master the foundations first by building real applications with simple, explainable tools.By the end of this course, you’ll not only understand how NLP works, but also have a complete project portfolio built entirely with Python and NLTK — ready to impress employers, clients, or fellow learners.

Overview

Section 1: Course Introduction & Setup

Lecture 1 What is NLP? Why It Matters

Lecture 2 What is NLTK and Why Learn It?

Lecture 3 Install Python, Jupyter & NLTK

Lecture 4 Downloading NLTK Resources

Lecture 5 Run Your First NLP Code

Lecture 6 Course Structure and Projects Walkthrough

Section 2: Text Preprocessing Essentials

Lecture 7 Introduction to Text Preprocessing

Lecture 8 Tokenization (Words & Sentences)

Lecture 9 Stopwords Removal

Lecture 10 Stemming

Lecture 11 Lemmatization

Lecture 12 Text Normalization (Lowercasing, Removing Punctuations)

Lecture 13 Full Text Preprocessing Pipeline

Lecture 14 Common Preprocessing Mistakes

Section 3: Working with Corpora

Lecture 15 What is a Corpus?

Lecture 16 Exploring the Gutenberg Corpus

Lecture 17 Analyzing the Reuters Corpus

Lecture 18 Brown Corpus and Genre Analysis

Lecture 19 Frequency Distributions

Lecture 20 Concordance, Collocations, and Dispersion

Lecture 21 Building Your Own TextCorpusReader

Lecture 22 Mini Project: Author Style Analyzer

Section 4: POS Tagging & Chunking

Lecture 23 Introduction to POS Tagging

Lecture 24 Using NLTK's pos_tag()

Lecture 25 Understanding POS Tagsets

Lecture 26 Custom POS Tagging using Tagged Corpora

Lecture 27 What is Chunking?

Lecture 28 Mini Project: Skills Extraction From Resume

Section 5: Text Classification with NLTK

Lecture 29 Introduction to Text Classification

Lecture 30 Bag of Words (BoW) Model

Lecture 31 Feature Extraction in NLTK

Lecture 32 Naive Bayes Classifier with NLTK

Lecture 33 Evaluating Classifier Performance

Lecture 34 Improving Feature Engineering

Section 6: Language Modeling & N-grams

Lecture 35 What is a Language Model?

Lecture 36 Introduction to N-grams

Lecture 37 Building a Basic N-gram Language Model

Lecture 38 Generating Text Using N-grams

Lecture 39 Mini Project: Build Your Own Shakespeare and Austen Emma Generator

Lecture 40 Mini Project: AutoComplete Like Feature

Section 7: Named Entity Recognition (NER) & Syntax Trees

Lecture 41 What is Named Entity Recognition (NER)?

Lecture 42 NLTK's Built-In NER with ne_chunk()

Lecture 43 Visualizing Parse Trees

Lecture 44 Extracting Named Entities from Trees

Section 8: Information Extraction & Regex

Lecture 45 What is Information Extraction?

Lecture 46 Intro to Regular Expressions (Regex) for NLP

Lecture 47 Extracting Common Entities with Regex

Lecture 48 Token and Phrase Pattern Matching with NLTK

Section 9: WordNet and Semantic Analysis

Lecture 49 Introduction to WordNet

Lecture 50 Exploring Synsets and Lemmas

Lecture 51 Synonyms, Antonyms, and Lemmas

Lecture 52 Hypernyms, Hyponyms, Meronyms

Lecture 53 Semantic Similarity Measures

Lecture 54 Word Sense Disambiguation (WSD)

Lecture 55 Mini Project: Synonym Sentence Swapper

Beginner Python programmers who want to get into Natural Language Processing (NLP) with hands-on, project-based learning.,Data science and AI students who are curious about how real-world text processing works using clean, foundational tools like NLTK.,Aspiring NLP engineers who want to build mini applications like spam classifiers, resume parsers, or text generators using only Python.,Academics or researchers looking for a practical and intuitive introduction to language modeling, tokenization, named entity recognition, and more.,Freelancers and job-seekers aiming to build NLP portfolio projects that demonstrate their skills in resume-friendly formats.,Anyone interested in language and text analysis who prefers building tools and learning by doing — without needing heavy machine learning or deep learning setups.