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Machine Learning for Humans

Vishal Maini and Samer Sabri, edited by Sachin Maini

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  • Demis HassabisRecommended as an accessible AI introduction
    In The Verge’s original 29 January 2019 reading list, Hassabis recommends this free guide as an accessible introduction to AI methods. This record uses the authors’ original 2017 ebook, with its editing credit; no later commercial edition is inferred.
    Source

Summary

This short 2017 guide introduces the main groups of machine-learning methods. It moves from supervised learning and model evaluation to clustering, neural networks and reinforcement learning, with examples, mathematics and code.

The authors wrote it for readers who want an initial map of the subject. The original series is also available as a free ebook. It supplies historical foundations and a path to further study; its examples and account of current AI reflect 2017.

  • SourceMachine Learning for Humans — original author introduction · Original2017 author introduction, complete roadmap, audience, linked free ebook and About the authors; linked PDF pp1–3 confirms both authors, Sachin Maini editing credit and19August2017 publication.
  • SourceMachine Learning for Humans — authors’ linked free ebook · Title page and contents pp1–3; full published roadmap. Ebook downloaded from the original author link.

Why read it Our view

Use the guide to tell different learning tasks apart and prepare better questions for a technical team. Check newer evidence before using its methods in a product. An introductory guide cannot establish that a proposed model is reliable or suited to your data.