Natural Language Processing

What you'll learn

• Explain core linguistic concepts (morphology, syntax, semantics) relevant to NLP
• Implement robust text preprocessing: tokenization, normalization, stemming, lemmatization, and cleaning
• Vectorize text using bag-of-words, TF-IDF, and embedding methods (word2vec, contextual embeddings)
• Build and evaluate machine learning models for sentiment analysis and text classification
• Develop named entity recognition (NER) systems using rule-based and ML approaches
• Train and fine-tune language models and generate coherent text
• Design basic dialogue systems and chatbots with intent recognition and response generation
• Use popular NLP libraries and frameworks (spaCy, NLTK, scikit-learn, Hugging Face, PyTorch/TensorFlow)
• Apply evaluation metrics, error analysis, and best practices for model validation
• Package and deploy simple NLP pipelines and demo applications

Prerequisites

Basic Python programming and familiarity with linear algebra, probability, and introductory machine learning.

Skills you'll gain

• Python programming for NLP
• Text preprocessing and normalization
• Feature engineering and vectorization
• Statistical and machine learning for text
• Deep learning for language tasks
• Word and contextual embeddings
• Language modeling and text generation
• Named entity recognition
• Sentiment analysis and text classification
• Dialogue system and chatbot design
• Model evaluation and error analysis
• Deploying NLP pipelines