Chapter 9: Natural Language Processing (NLP)

🧠 NLP = “Giving machines the ability to read, understand, and generate human language.”

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🔹 1. Text Preprocessing (Cleaning Text Before Feeding to Models)

Key Steps:

Step	Purpose
Tokenization	Split text into words or sentences
Lowercasing	Uniform case for comparison
Stopword Removal	Remove common words (like "is", "the")
Stemming	Reduce words to root (e.g., "playing" → "play")
Lemmatization	Similar to stemming, but more accurate
Removing Punctuation & Numbers	Clean irrelevant symbols

Example (Using NLTK):

python
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer

text = "Cats are playing with the ball."
tokens = word_tokenize(text.lower())
filtered = [w for w in tokens if w not in stopwords.words('english')]
stemmer = PorterStemmer()
stemmed = [stemmer.stem(w) for w in filtered]

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🔹 2. Text Representation (Feature Engineering)

Technique	Description
Bag of Words (BoW)	Count how often each word appears
TF-IDF	Weighs rare but important words higher
Word2Vec	Converts words to dense vectors with meaning
GloVe	Pretrained word vectors from huge corpora
BERT/GPT Embeddings	Deep contextual representations

📌 Goal: Convert text into numbers so ML/DL models can understand.

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🔹 3. NLP Models & Techniques

Task	Model/Algorithm
Sentiment Analysis	Logistic Regression, LSTM, BERT
Text Classification	Naive Bayes, SVM, CNN
Named Entity Recognition (NER)	SpaCy, Transformers
Machine Translation	Sequence-to-sequence, Transformer
Text Summarization	Seq2Seq + Attention, Pegasus
Question Answering	BERT, GPT models
Chatbots	RNN + DialogFlow, GPT-4, Retrieval-based

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🔹 4. Transformers, BERT & GPT

🔸 Transformers

Use self-attention to understand context in sequences.

• Core of modern NLP

• Input can be processed in parallel, unlike RNNs

🔸 BERT (Bidirectional Encoder Representations from Transformers)

• Reads text in both directions (context-aware)

• Fine-tuned for:

  • Sentiment Analysis

  • Question Answering

  • NER

🔸 GPT (Generative Pretrained Transformer)

• Focuses on text generation

• Examples: GPT-2, GPT-3, GPT-4

• Used in ChatGPT, AI writers, etc.

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🔹 5. Hands-on Projects in NLP

Project	Tools & Models
Chatbot	RNN/Transformer, Preprocessed text
Sentiment Analysis	LSTM/BERT + IMDB dataset
Text Summarizer	Seq2Seq + Attention
Spam Classifier	TF-IDF + Logistic Regression
Question Answering Bot	BERT/Q&A Dataset

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🔹 6. Popular Libraries for NLP

Library	Purpose
NLTK	Preprocessing & linguistic tasks
spaCy	Fast NLP tasks (NER, POS tagging)
Scikit-learn	BoW, TF-IDF + ML models
Transformers (HuggingFace)	Pretrained models (BERT, GPT, RoBERTa)
TextBlob	Simple NLP operations
OpenAI API	GPT-based text generation

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✅ Summary of Chapter 9

Topic	Summary
Text Preprocessing	Clean, tokenize, remove noise
Text Vectorization	Convert text to numeric (BoW, TF-IDF, Word2Vec, BERT)
NLP Models	Classification, generation, translation
Transformers	State-of-the-art for all major NLP tasks
Libraries	HuggingFace, NLTK, spaCy, OpenAI API

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💡 Mini Tasks:

1. Build a spam/ham classifier using Naive Bayes + TF-IDF.

2. Train a sentiment analyzer using LSTM or BERT on IMDB data.

3. Create a simple chatbot using nltk.chat or Transformer.

4. Use HuggingFace to load a BERT model for Q&A.