Natural Language Processing, or NLP, is a branch of artificial intelligence that focuses on the interaction between computers and humans through natural language. It enables machines to understand, interpret, and generate human language in a way that is both meaningful and useful.

📱 Applications of NLP

  • Chatbots and virtual assistants (e.g., Siri, Alexa)
  • Sentiment analysis for social media monitoring
  • Language translation services (e.g., Google Translate)
  • Text summarization and document classification
  • Speech recognition systems

🧠 Core Concepts in NLP

  1. Tokenization: Splitting text into words, phrases, or symbols.
  2. Syntax Analysis: Understanding sentence structure (e.g., parsing grammar).
  3. Semantic Analysis: Extracting meaning from text.
  4. Machine Learning Models: Algorithms like RNNs, Transformers, and BERT.
  5. Preprocessing: Removing noise, stemming, and lemmatization.

🛠️ Tools & Resources

  • Python libraries: NLTK, spaCy, Hugging Face Transformers
  • APIs: Google Cloud Natural Language API, Azure Text Analytics
  • Datasets: Common Crawl, Wikipedia, IMDb reviews
  • Frameworks: TensorFlow, PyTorch, Keras

For a deeper dive into NLP fundamentals, check out our NLP Overview page.

NLP Tutorial

Explore advanced topics like deep learning in NLP by visiting Machine Learning Basics.

Transformers Model

Want to see real-world examples? Look at NLP Use Cases for practical applications.