Natural Language Processing

What You Will Learn

• 1 Learn the foundations of Natural Language Processing, including core terminology, linguistic concepts, and the NLP pipeline that underpins every modern text application.
• 2 Understand text preprocessing techniques such as tokenization, stemming, lemmatization, stop-word removal, and normalization that prepare raw text for downstream analysis.
• 3 Apply classical text representation methods including Bag of Words, TF-IDF, and n-grams to convert unstructured text into structured numerical features for machine learning models.
• 4 Explore word embeddings and distributed representations such as Word2Vec, GloVe, and FastText that capture semantic relationships between words in continuous vector spaces.
• 5 Understand sequence modeling with Recurrent Neural Networks and Long Short-Term Memory networks to handle temporal dependencies in language data.
• 6 Analyze the Transformer architecture and the attention mechanism that power state-of-the-art language models and have redefined performance benchmarks across NLP tasks.
• 7 Apply pre-trained language models such as BERT, GPT, and their fine-tuned variants to solve real-world NLP tasks with minimal task-specific training data.
• 8 Build text classification and sentiment analysis systems using both traditional machine learning pipelines and deep learning approaches.
• 9 Implement Named Entity Recognition, Part-of-Speech tagging, and dependency parsing to extract structured information from unstructured text.
• 10 Develop question answering, summarization, and machine translation systems that demonstrate generative and extractive language generation capabilities.
• 11 Understand evaluation metrics, bias detection, and fairness considerations essential for building responsible and production-ready NLP systems.
• 12 Create an end-to-end NLP project by integrating preprocessing, modeling, evaluation, and deployment strategies into a complete application pipeline.