Mastering NLP

# Mastering NLP

## Part I: Introduction to Natural Language Processing
- What is NLP?
  - Definition and Scope
  - Historical Background
  - Applications in Real Life
- Key Concepts in NLP
  - Tokenization
  - Lemmatization and Stemming
  - Stop Words Removal
- Challenges in NLP
  - Ambiguity in Language
  - Context Understanding
  - Multilingual Processing

## Part II: Text Preprocessing Techniques
- Data Cleaning
  - Handling Missing Data
  - Removing Noise (HTML tags, special characters)
  - Normalization
- Feature Extraction
  - Bag of Words (BoW)
  - Term Frequency-Inverse Document Frequency (TF-IDF)
  - Word Embeddings (Word2Vec, GloVe)
- Dimensionality Reduction
  - Principal Component Analysis (PCA)
  - Latent Semantic Analysis (LSA)
  - t-Distributed Stochastic Neighbor Embedding (t-SNE)

## Part III: Core NLP Tasks
- Text Classification
  - Sentiment Analysis
  - Spam Detection
  - Topic Modeling
- Named Entity Recognition (NER)
  - Identifying People, Locations, Organizations
  - Using SpaCy and NLTK Libraries
  - Customizing NER Models
- Part-of-Speech Tagging
  - Grammar Rules and Syntax Trees
  - Dependency Parsing
  - Practical Use Cases

## Part IV: Advanced Topics in NLP
- Sequence-to-Sequence Models
  - Encoder-Decoder Architecture
  - Attention Mechanism
  - Transformer Models
- Machine Translation
  - Statistical Machine Translation (SMT)
  - Neural Machine Translation (NMT)
  - Evaluation Metrics (BLEU Score)
- Question Answering Systems
  - Open-Domain vs Closed-Domain QA
  - Retrieval-Based and Generative Approaches
  - Building a Chatbot with NLP

## Part V: Tools and Frameworks for NLP
- Popular Libraries and APIs
  - NLTK
  - SpaCy
  - Hugging Face Transformers
- Cloud-Based Solutions
  - Google Cloud NLP API
  - AWS Comprehend
  - Microsoft Azure Text Analytics
- Best Practices for Implementation
  - Choosing the Right Model
  - Optimizing Performance
  - Ethical Considerations in NLP

## Part VI: Future Trends in NLP
- Emerging Technologies
  - Transfer Learning in NLP
  - Few-Shot and Zero-Shot Learning
  - Multimodal Models
- Industry Applications
  - Healthcare
  - Finance
  - Customer Support Automation
- Research Directions
  - Explainability in NLP
  - Bias Mitigation
  - Cross-Lingual Generalization