📘 NLP Notes PDF – Complete Semester Syllabus

🧩 Unit I – Language Processing and Python

This unit introduces the foundations of computational linguistics using Python. You’ll learn how to process and analyze textual data programmatically.

Topics Covered:

• Computing with Language: Texts and Words
• Python for NLP: Texts as Lists of Words
• Simple Statistics with Language Data
• Making Decisions in Python: Conditional statements and control structures
• Automatic Natural Language Understanding

Accessing Text and Lexical Resources:

• Accessing Text Corpora
• Conditional Frequency Distributions
• Lexical Resources and WordNet

🧠 Unit II – Text Processing and Word Categorization

This unit focuses on text preprocessing — the first and most essential step in NLP workflows.

Topics Covered:

• Accessing Text from the Web or Disk
• Text Processing with Unicode
• Regular Expressions: Detecting and tokenizing patterns
• Normalization and Segmentation
• Formatting: From Lists to Strings

Categorizing and Tagging Words:

• Using a Tagger and Tagged Corpora
• Python Dictionaries for Mapping Word Properties
• Automatic Tagging, N-Gram Tagging, and Transformation-Based Tagging
• Determining Word Categories

🤖 Unit III – Text Classification and Deep Learning

Unit III introduces the power of Machine Learning and Deep Learning in NLP.

Topics Covered:

• Supervised Classification
• Evaluation Techniques
• Naive Bayes Classifiers

Deep Learning for NLP:

• Introduction to Deep Learning
• Convolutional Neural Networks (CNNs) for Text
• Recurrent Neural Networks (RNNs)
• Text Classification using Deep Learning

🔍 Unit IV – Information Extraction and Syntax Analysis

This unit teaches how to extract structured information from unstructured text and analyze sentence structure grammatically.

Extracting Information from Text:

• Information Extraction and Chunking
• Developing and Evaluating Chunkers
• Recursion in Linguistic Structure
• Named Entity Recognition (NER)
• Relation Extraction

Analyzing Sentence Structure:

• Grammatical Dilemmas and Use of Syntax
• Context-Free Grammar (CFG)
• Parsing with CFG

Why is Natural Language Processing Important for Engineers?

Natural Language Processing (NLP) is the backbone of modern Artificial Intelligence. As a B.Tech CSE AI student, learning how machines interpret, process, and generate human language is essential. Whether you are building advanced chatbots, automated sentiment analysis tools, or designing algorithms like Chat-GPT, the deep learning foundations covered in these notes—like Recurrent Neural Networks (RNNs) and Context-Free Grammars—are highly sought-after skills in today's machine learning job market.

Frequently Asked Questions (FAQ)

• Are these notes updated for the latest B.Tech syllabus?
  Yes, these NLP handwritten PDF notes cover the complete syllabus for Computer Science Engineering students (including Gurugram University's AI curriculum).
• Do I need to know Python before studying NLP?
  While basic programming knowledge helps, Unit I specifically covers processing language data effectively using Python and the NLTK library.
• Where can I find other related study materials?
  You can also download our Semester 5 PYQ Question Papers to test your knowledge, or view our Text and Web Intelligence notes for related web-mining topics!