Embeddings

Lesson in the Machine Learning Roadmap · Stage 38: Natural Language Processing

Last updated 14 September 2026

What is Embeddings?

Embeddings are a way to represent words, phrases, or even entire documents as numerical vectors in a high-dimensional space. The goal is to capture the semantic meaning of the text, so that words with similar meanings are located close to each other in the vector space. This allows machine learning models to understand relationships between words and perform tasks like text classification, sentiment analysis, and machine translation more effectively.

Study this in the interactive course See the whole Machine Learning roadmap

Free resources to learn Embeddings

ArticleGetting Started With Embeddings
ArticleA Guide on Word Embeddings in NLP

Embeddings: common questions

Can I learn Embeddings in Hindi?

This lesson is written in English. Once you sign in, open it in the interactive course and ask the AI tutor to explain Embeddings in Hindi or Hinglish. The tutor answers from this lesson’s own text, so it stays on the syllabus, and it runs on your own Google, OpenAI or Anthropic key.

Is this Embeddings tutorial for beginners?

It is lesson 148 of 150 in the free Machine Learning roadmap, in stage 38, Natural Language Processing. If you are new to Machine Learning, start from stage 1 and come back here in order; if you already know the earlier stages, this lesson stands on its own.