Introduction to Natural Language Processing

What you'll learn

• Preprocess and clean text data (tokenization, normalization, stop-word handling)
• Apply linguistic concepts such as POS tagging, parsing, and semantics
• Use statistical methods for language modeling and sequence tagging
• Train and evaluate machine learning models for text classification and sequence prediction
• Implement deep learning approaches including word embeddings and transformer-based models
• Build simple natural language generation systems and understand decoding strategies
• Work with speech data and basic speech processing techniques
• Perform information retrieval and construct basic search pipelines
• Evaluate NLP systems using appropriate metrics and error analysis
• Design and complete practical NLP projects using common tools and libraries

Prerequisites

Basic programming experience (preferably Python) and familiarity with basic probability and linear algebra.

Skills you'll gain

• Text preprocessing
• Tokenization and normalization
• Part-of-speech tagging and parsing
• Statistical language modeling
• Supervised machine learning for NLP
• Deep learning for NLP (embeddings, transformers)
• Natural language generation
• Speech processing fundamentals
• Information retrieval
• Model evaluation and error analysis
• Python for NLP development