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Introduction to NLP

Teaching computers to understand human language

What is Natural Language Processing?

NLP is a subfield of AI that focuses on enabling computers to understand, interpret, and generate human language in meaningful ways. It combines linguistics, computer science, and machine learning.

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Speech Recognition

Converting audio to text

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Text Analysis

Understanding written text

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Translation

Automated language translation

💡 Fun Fact: The term NLP was coined in the 1960s, but modern deep learning transformed it starting with Word2Vec in 2013!
🎯 Knowledge Check

What does NLP stand for?

Which is a real NLP application?

What year was Word2Vec introduced?

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Tokenization

Breaking text into meaningful pieces

What is Tokenization?

Tokenization is the process of breaking down text into smaller units (tokens) like words, sentences, or subwords. It's a fundamental preprocessing step in NLP.

✨ Try Tokenization
🎯 Knowledge Check

What is tokenization?

Why is tokenization important?

What's the difference between word and subword tokenization?

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Word Embeddings

Converting words into numeric representations

Understanding Word Embeddings

Word embeddings represent words as dense vectors of numbers. Similar words have similar vectors, allowing models to understand semantic relationships.

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Word2Vec

Skip-gram & CBOW models

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GloVe

Global vectors for word representation

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FastText

Subword information embeddings

Key Figure

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Tomas Mikolov
Born 1987

Contribution: Created Word2Vec at Google Brain (2013); introduced concept: "king - man + woman = queen"

Why it mattered: Made linguistic meaning computable and enabled the neural NLP revolution

Milestone: 2013 — Word2Vec
"King - man + woman = queen" shocked the NLP world; vector arithmetic unlocked semantic understanding at scale

🎯 Knowledge Check

What are word embeddings?

Which technique was developed by Google in 2013?

What property do similar words have in embeddings?

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Sentiment Analysis

Understanding emotions and opinions in text

What is Sentiment Analysis?

Sentiment analysis determines the emotional tone or opinion expressed in text. Applications include social media monitoring, customer feedback, and review analysis.

🔍 Analyze Sentiment
🎯 Knowledge Check

What does sentiment analysis determine?

What's a real-world application?

How many sentiment classes typically exist?

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Advanced NLP

Modern transformers and large language models

The Transformer Revolution

Transformers (2017) introduced attention mechanisms, enabling parallel processing and better context understanding. They power modern LLMs like BERT, GPT, and T5.

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BERT

Bidirectional encoder representations

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GPT

Generative pre-trained transformers

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T5

Text-to-text transfer transformer

# Pseudo-code for transformer query = embed(input_text) key = embed(context) attention = softmax(query @ key.T) output = attention @ value
🎯 Final Knowledge Check

What year was the Transformer introduced?

What key innovation did Transformers introduce?

Which is a modern transformer-based model?

🚀 What's Next?

You've mastered Natural Language Processing! Continue your AI journey by exploring specialized domains and advanced applications.

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Course 9: Reinforcement Learning

Train AI agents to learn from interactions

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Course 6: Generative AI

Learn about GANs, diffusion, and LLMs

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Course 7: Computer Vision

Learn how AI systems see and understand images

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Back to Dashboard

Review your progress and explore other courses

📚 Course Resources
→ "Attention Is All You Need" Paper (Vaswani et al., 2017)
→ HuggingFace NLP Course
→ Stanford CS224N: NLP with Deep Learning