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Machine LearningBook

by Tom M. Mitchell

Who uses, recommends or left Machine Learning, in their own words: each a verbatim quote with a link to where it was said.

1 person

Machine learning engineer and independent AI consultant; writes about LLM evaluation and tooling at hamel.dev.
recommended

So this one is very classic one. Machine learning by Mitchell. Now, it's kind of theoretical, but the thing I like about it is it um really drives home the fact that AAM's razor is prevalent not only in like science but also in machine learning and AI. So, a lot of times the simplest and also engineering. So like a lot of times the simpler approach generalizes better. And so that's the thing I kind of internalized deeply from that book.

Lenny's Podcast, "Why AI evals are the hottest new skill for product builders", lightning round, asked for the books they recommend most; the guest's own spoken answer (YouTube captions).

2025-09-25·youtube.com ↗

Every entry is a verbatim quote and a link to the public post it came from. Nothing is paraphrased. If it is not on record somewhere public, it is not here.

The verdict is what they actually said: loved and liked are explicit; recommended means they told others to get it; uses means they only say they use it; read is a book on their public shelf with no verdict; mixed and disliked are kept too.

A dashed affiliate link, sponsored or their own label means the post disclosed a relationship. It is shown, never hidden.

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The shading behind a date shows how dated a recommendation is, for its kind of thing. Fully green is recent. It drains a quarter at a time as the post gets older, and is empty once it reads as dated.

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