Siksha Sarovar

Siksha Sarovar (sikshasarovar.com) is a free educational web application that helps students in India learn programming and prepare for academic and competitive exams. The platform offers structured coding courses (C, C++, Python, Java, HTML, CSS, PHP, Power BI, AI, Machine Learning, Data Science), complete university curriculum notes for BCA/MCA students with previous year question papers, Class 10 and Class 12 CBSE/HBSE school notes, and dedicated preparation material for SSC, UPSC, Banking, Railway and other government exams. Browsing the site is completely free and requires no account. Users may optionally sign in with Google solely to save their learning progress, quiz scores and personal preferences across devices.

Privacy Policy | Terms of Service | Contact Siksha Sarovar | About Siksha Sarovar

v4.0.9 · PWA
Siksha Sarovar logo
Siksha Sarovar
Your Learning Universe

Siksha Sarovar is a free e-learning platform for coding courses, BCA university notes and competitive exam preparation. Optional Google sign-in saves your learning progress across devices.

Initializing knowledge base…
Compiling modules 0%

About This Course

Lesson 1 of 39 in the free Natural Language Processing notes on Siksha Sarovar, written by Rohit Jangra.

Natural Language Processing — University Course

This course covers Natural Language Processing (NLP) as taught in undergraduate/postgraduate BCA/MCA/BTech university courses. The syllabus is divided into 4 units — introduction and text preprocessing, syntactic/semantic analysis with language models, feature extraction and text classification, and word embeddings with deep learning, transformers, and real-world applications.

Syllabus at a Glance:

UnitHoursTopics
Unit 112Scope, challenges, applications; levels of language processing; tokenization, normalization, stemming, lemmatization, stop-word removal; regular expressions
Unit 212POS tagging, Context-Free Grammar (CFG), syntactic parsing, Named Entity Recognition (NER), Word Sense Disambiguation (WSD), language models & n-grams
Unit 312Feature extraction (BoW, TF-IDF), text classification with Naïve Bayes, sentiment analysis, information extraction, text summarization
Unit 410Word2Vec embeddings, deep learning for NLP, Transformers & LLMs, applications: sentiment analysis, machine translation, QA, chatbots, speech assistants, generative AI

What you will learn:

  • How machines process, understand, and generate human language, and why it is a genuinely hard problem
  • Every stage of a text preprocessing pipeline — tokenization through stop-word removal — with runnable Python code
  • POS tagging, grammar-based parsing, named entity recognition, word sense disambiguation, and n-gram language models
  • How to turn text into numeric features (BoW, TF-IDF) and classify/summarize/extract information from it
  • Word embeddings (Word2Vec), the Transformer architecture, and how modern LLMs and generative AI applications are built on these foundations
PYQ papers are available at the end of the lesson list.