Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through natural language. This tutorial will guide you through the fundamentals of NLP, its applications, and practical implementation steps.

Key Concepts in NLP 🧠

  • Text Preprocessing
    Clean and normalize raw text data using techniques like tokenization, stemming, and stopword removal.

    Text_Preprocessing

  • Feature Engineering
    Convert text into numerical representations (e.g., TF-IDF, word embeddings) for machine learning models.

    Feature_Engineering

  • Model Training
    Use algorithms like Naive Bayes, SVM, or deep learning frameworks (e.g., Transformers) to build NLP models.

    Model_Training

Applications of NLP 💡

  • Machine Translation
    Machine_Translation
  • Sentiment Analysis
    Sentiment_Analysis
  • Chatbots & Virtual Assistants
    Chatbots_Virtual_Assistants

Get Started with NLP 🚀

  1. Learn basics of Machine Learning to understand NLP foundations.
  2. Explore Deep Learning for NLP for advanced techniques.
  3. Practice with real-world datasets and tools like spaCy or Hugging Face Transformers.

For further reading, check out our AI & NLP Resources section! 📖