Welcome to the Natural Language Processing (NLP) course! This guide will help you explore the fundamentals of NLP, its applications, and key concepts. 🧠
What is NLP?
Natural Language Processing is a field of artificial intelligence focused on enabling machines to understand, interpret, and generate human language.
Learning Objectives
- Understand the basics of text preprocessing and tokenization.
- Learn about common NLP tasks like sentiment analysis and machine translation.
- Explore tools and libraries such as spaCy and NLTK.
- Apply NLP techniques to real-world problems.
Course Content
- Introduction to NLP
- History and evolution of NLP
- Key challenges in language understanding
- Text Processing
- Tokenization, stemming, and lemmatization
- Stop words removal and text normalization
- Machine Learning for NLP
- Supervised vs. unsupervised learning in language models
- Deep learning approaches (e.g., RNNs, Transformers)
Practical Applications
- Chatbots and virtual assistants
- Sentiment analysis in social media
- Language translation services
- Text summarization tools
Resources
- Documentation/en/Explore/Courses/Introduction_to_Machine_Learning for foundational ML concepts.
- Natural_Language_Processing_Books for recommended reading.
- NLP_Tools_and_Libraries to dive deeper into practical implementations.
Let me know if you'd like to explore specific topics further! 🚀