Andrei Burkov

Machine Learning Team Lead — TalentNeuron Founder/Author — True Positive Inc

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Andriy Burkov is the rare author who kept his promise: a machine learning textbook that actually fits in a hundred pages. Published in 2019, The Hundred-Page Machine Learning Book hit #1 on Amazon, got translated into more than a dozen languages, and now sits on reading lists at universities worldwide. Andrew Ng blurbed it. Peter Norvig blurbed it. For a self-published book from a working engineer, that’s the full set.

Burkov holds a PhD in AI and spent the early part of his career running production ML projects at Fujitsu and Gartner. He’s now Head of Machine Learning at TalentNeuron, where his team analyzes billions of job postings across 30+ languages in near-real-time. It’s the kind of grind-it-out applied ML work that informs the book’s perspective — less “here’s the theory,” more “here’s what actually breaks in production.”

In 2020 he followed up with Machine Learning Engineering, the sequel aimed at the MLOps and systems side. In January 2025 came The Hundred-Page Language Models Book — the same compressed-textbook treatment applied to transformers and LLMs, code-centric with PyTorch and Google Colab. Vint Cerf called it “a gem of clarity.” Tomáš Mikolov (word2vec) endorsed it. Jerry Liu of LlamaIndex called it one of the most comprehensive concise handbooks for understanding LLMs under the hood.

If you’re looking for an on-ramp to ML, Burkov’s books are the shortest honest path. They don’t pretend the field is easy, but they refuse to hide behind the complexity. For the father-with-decades-of-code half of the audience, the 100-page ML book is a weekend. For the AI student half, the LM book is a code-first refresher that complements the formal coursework. Either way: no filler.