Introduction:
- Natural Language Processing (NLP) in AI
- Integration in Business Applications
Key Components:
- Speech Recognition
- Natural Language Understanding (NLU)
- Natural Language Generation (NLG)
NLP Techniques:
- Tokenization
- Part-of-Speech Tagging
- Named Entity Recognition
- Sentiment Analysis
- Machine Translation
- Text Classification
Recent Advances:
- Deep Learning Impact
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
Working of NLP:
- Speech Recognition Process
- NLU Challenges and Techniques
- NLG Structure and Text-to-Speech
Roles in NLP:
- NLP Engineer
- NLP Researcher
- ML Engineer
- NLP Data Scientist
- NLP Consultant
NLP Technologies:
- Machine Learning
- Natural Language Toolkits (NLTK)
- Parsers
- Text-to-Speech (TTS) and Speech-to-Text (STT) Systems
- Named Entity Recognition (NER) Systems
Applications of NLP:
- Spam Filters
- Algorithmic Trading
- Question Answering
- Summarizing Information
Future Scope:
- Chatbot Advancements
- Supporting Invisible UI
- Smarter Search with NLP