NLP Fundamentals
Build a solid foundation in Natural Language Processing. This path covers the core concepts from traditional NLP through to modern LLM-based approaches, including tokenization, embeddings, classification, extraction, and generation.
What You'll Learn
- Understand how computers process and understand human language
- Learn tokenization, embeddings, and text representation
- Build text classification and sentiment analysis systems
- Implement named entity recognition and information extraction
- Use LLMs for advanced NLP tasks
- Evaluate NLP system performance effectively
Course Lessons
Introduction to Natural Language Processing
18 min readUnderstand what NLP is, its core challenges, and how the field has evolved from rule-based systems to neural approaches and LLMs.
Tokenization and Text Representation
20 min readLearn how text is converted to numbers — tokenization methods, word embeddings, sentence embeddings, and modern tokenizers like BPE.
Text Classification and Sentiment Analysis
20 min readBuild systems that categorize text and detect sentiment using both traditional ML approaches and LLM-based methods.
Named Entity Recognition and Information Extraction
18 min readExtract structured information from unstructured text — identifying names, dates, organizations, and custom entity types.
Text Summarization and Generation
18 min readImplement abstractive and extractive summarization, and understand the generation capabilities of modern language models.
Semantic Search and Question Answering
20 min readBuild systems that understand meaning, not just keywords — semantic search engines and question answering systems using embeddings and LLMs.
Read lesson →Evaluating NLP Systems
15 min readMeasure NLP system quality using appropriate metrics — BLEU, ROUGE, F1, and human evaluation techniques for generative tasks.
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