Welcome to the Natural Language Processing (NLP) course! This path is designed to help you explore the fascinating world of NLP, where machines learn to understand, interpret, and generate human language. 🌐
📚 Course Overview
NLP combines machine learning and linguistics to enable computers to process and analyze text data. Key topics include:
- Tokenization & Sentiment Analysis
- Language Models (e.g., BERT, GPT)
- Named Entity Recognition
- Text Generation & Chatbots
🎯 Learning Objectives
- Understand the fundamentals of NLP
- Master key algorithms and frameworks
- Apply NLP techniques to real-world problems
- Explore ethical considerations in language processing
📋 Course Syllabus
Introduction to NLP
- History and applications
- Key challenges (e.g., ambiguity, context)
Core Techniques
- Text preprocessing (stemming, lemmatization)
- Part-of-speech tagging
- Dependency parsing
- Machine learning models for NLP
Advanced Topics
- Deep learning architectures (RNNs, Transformers)
- Pretrained models and fine-tuning
- Multilingual NLP systems
- NLP in industry (e.g., customer support, search engines)
📘 Recommended Resources
- Natural Language Processing with Python (book)
- Deep Learning for NLP (advanced tutorial)
- Hugging Face NLP Course (external link)
🤖 Hands-On Projects
- Build a sentiment analysis tool
- Create a chatbot using conversational models
- Analyze social media trends with topic modeling
For further exploration, check out our AI Tutorial Section! 🌟