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NLP is an open source introductory resource for natural language processing, presented as a continuously updated book hosted on GitHub. It explains how machines process and understand human language, combining theory with practical examples. Its covers core NLP concepts such as text representation, feature extraction, and model evaluation, alongside hands-on implementations using tools like Word2Vec, TF-IDF, and FastText. It also introduces topic modeling with LDA, keyword extraction techniques, and document similarity methods. NLP extends into real-world applications, including sentiment analysis and text classification, helping readers connect concepts to use cases. Designed for accessibility, the project evolves over time, allowing updates and improvements as NLP techniques advance. It reflects a practical approach to learning, where readers can explore code, experiment with models, and build foundational skills in machine learning-driven language processing.
Features
- Open-source NLP beginner book developed and updated on GitHub
- Covers datasets, evaluation methods, and NLP fundamentals
- Includes models like TF-IDF, Word2Vec, Doc2Vec, and FastText
- Demonstrates topic modeling using LDA and keyword extraction methods
- Provides practical tasks such as sentiment analysis and classification
- Combines theory with hands-on Python-based implementations
Project Samples
