Understanding NLP is key to working with language-based AI systems like chatbots, translators, and voice assistants.
1️⃣ What is NLP?
NLP stands for Natural Language Processing. It enables machines to understand, interpret, and respond to human language.
2️⃣ Key NLP Tasks:
• Text classification (spam detection, sentiment analysis)
• Named Entity Recognition (NER) (identifying names, places)
• Tokenization (splitting text into words/sentences)
• Part-of-speech tagging (noun, verb, etc.)
• Machine translation (English → French)
• Text summarization
• Question answering
3️⃣ Tokenization Example:
from nltk.tokenize import word_tokenize
text = "ChatGPT is awesome!"
tokens = word_tokenize(text)
print(tokens) # ['ChatGPT', 'is', 'awesome', '!']
4️⃣ Sentiment Analysis:
Detects the emotion of text (positive, negative, neutral).
from textblob import TextBlob
TextBlob("I love AI!").sentiment # Sentiment(polarity=0.5, subjectivity=0.6)
5️⃣ Stopwords Removal:
Removes common words like “is”, “the”, “a”.
from nltk.corpus import stopwords
words = ["this", "is", "a", "test"]
filtered = [w for w in words if w not in stopwords.words("english")]
6️⃣ Lemmatization vs Stemming:
• Stemming: Cuts off word endings (running → run)
• Lemmatization: Uses vocab grammar (better results)
7️⃣ Vectorization:
Converts text into numbers for ML models.
• Bag of Words
• TF-IDF
• Word Embeddings (Word2Vec, GloVe)
8️⃣ Transformers in NLP:
Modern NLP models like BERT, GPT use transformer architecture for deep understanding.
9️⃣ Applications of NLP:
• Chatbots
• Virtual assistants (Alexa, Siri)
• Sentiment analysis
• Email classification
• Auto-correction and translation
🔟 Tools/Libraries:
• NLTK
• spaCy
• TextBlob
• Hugging Face Transformers
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