Dimensionality Reduction
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What is Dimensionality Reduction?
Dimensionality reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables. It can be divided into feature selection and feature extraction. Feature selection selects a subset of the original features, while feature extraction transforms the data into a lower-dimensional space. The goal is to simplify the data without losing important information, making it easier to analyze and model.
Free resources to learn Dimensionality Reduction
More in stage 21: Dimensionality Reduction
Dimensionality Reduction: common questions
Can I learn Dimensionality Reduction 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 Dimensionality Reduction 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 Dimensionality Reduction tutorial for beginners?
It is lesson 87 of 150 in the free Machine Learning roadmap, in stage 21, Dimensionality Reduction. 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.