Course Curriculum
10 interactive lessons with real-time autograding
1
Module 1
Introduction to NLP & NLTK
NLP core concepts, corpora, and environment setup in Python.
2
Module 2
Tokenisation
Splitting text into words and sentences with word_tokenize and sent_tokenize.
3
Module 3
Cleaning Text
Stop words removal, lowercasing, punctuation filtering, and text normalization.
4
Module 4
Stemming & Lemmatisation
Porter, Snowball stemmers, and WordNetLemmatizer with POS tags.
5
Module 5
Part-of-Speech Tagging
Penn Treebank POS tagging, tag patterns, and grammar analysis.
6
Module 6
Frequencies & N-grams
FreqDist, bigrams, trigrams, and collocation discovery.
7
Module 7
WordNet
Synsets, hypernyms, hyponyms, and semantic similarity scoring.
8
Module 8
Named Entity Recognition
Chunking, chinking, and ne_chunk for extracting entities.
9
Module 9
Sentiment Analysis
VADER sentiment intensity analyzer, polarity scores, and social text.
10
Module 10
Text Classification
Feature extraction, Naive Bayes classifier, and model evaluation.