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Dev tools · December 1, 2022

NL

Natural Language Processing Demystified

Link: https://www.nlpdemystified.org/ Hi HN: After a year of work, I've published my free NLP course. The course helps anyone who knows Python and a bit of math go from the basics to today's mainstream models and frameworks. I strive to balance theory and practice, so every module consists of detailed explanations and slides along with a Colab notebook putting the ideas into practice (in most modules). The notebooks cover how to accomplish everyday NLP tasks including extracting key information, document search, text similarity, text classification, finding topics in…

Alternativestop 5% of December 2022

In plain words

Natural Language Processing Demystified is a free online course for Python developers with basic math knowledge seeking to learn NLP. It covers foundational concepts through modern models and frameworks, combining theoretical explanations with practical Colab notebooks. The course teaches classical and contemporary approaches to common tasks including text classification, document search, information extraction, summarization, translation, and question answering. Modules progress from text preprocessing and numerical representation to advanced techniques.

written from the facts on this page · September 2026

From the sources

In the maker’s words, at launch

Link: https://www.nlpdemystified.org/ Hi HN: After a year of work, I've published my free NLP course. The course helps anyone who knows Python and a bit of math go from the basics to today's mainstream models and frameworks. I strive to balance theory and practice, so every module consists of detailed explanations and slides along with a Colab notebook putting the ideas into practice (in most modules). The notebooks cover how to accomplish everyday NLP tasks including extracting key information, document search, text similarity, text classification, finding topics in documents, summarization, translation, generating text, and question answering. The course is divided into two parts. In part one, we cover text preprocessing, how to turn text into numbers, and multiple ways to classify and search text using "classical" approaches. And along the way, we'll pick up valuable bits on how to use tools such as spaCy and scikit-learn. In part two, we dive into deep learning for NLP. We start with neural network fundamentals and go through embeddings and sequence models until we arrive at transformers and the mainstream models of today. No registration required: https://www.nlpdemystified.org/

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