Natural Language Processing (NLP): Teaching Machines to Understand Text

Natural Language Processing (NLP) is the AI field that focuses on the interaction between computers and human language. It combines linguistics, computer science, and deep learning to interpret and generate text and speech.

Core Concepts in NLP
Tokenization: Breaking down text into words or sentences.
Part-of-Speech Tagging: Identifying nouns, verbs, etc.
Named Entity Recognition (NER): Detecting names, dates, and organizations.
Word Embeddings: Representing words as vectors (Word2Vec, GloVe).
Transformers: Attention-based models that revolutionized NLP (BERT, GPT).

Applications
Chatbots and Virtual Assistants: Alexa, Siri, and customer support bots.
Translation: Google Translate and multilingual communication.
Sentiment Analysis: Understanding customer feedback.
Content Summarization: Extracting insights from large documents.

Modern Advancements
Pre-trained language models.
Fine-tuning for domain-specific tasks.
Zero-shot and few-shot learning.

Ethical Considerations
Bias in language models.
Misinformation and deepfakes.

Demo Video
Walkthrough: https://www.youtube.com/watch?v=8dLF3nzBD_s

NLP is transforming communication by bridging human language with computational intelligence.