Iโve spent the past few days reading ๐๐ฉ๐ฆ ๐๐ฏ๐ฆ-๐๐ถ๐ฏ๐ฅ๐ณ๐ฆ๐ฅ-๐๐ข๐จ๐ฆ ๐๐ข๐ค๐ฉ๐ช๐ฏ๐ฆ ๐๐ฆ๐ข๐ณ๐ฏ๐ช๐ฏ๐จ Book by Andriy Burkov, and Iโm genuinely glad I did.
This is one of those rare books that manages to strike what feels like a ๐ฝ๐ฒ๐ฟ๐ณ๐ฒ๐ฐ๐ ๐ฏ๐ฎ๐น๐ฎ๐ป๐ฐ๐ฒ: it covers a remarkably wide range of machine learning algorithms and techniques in a very short space, explains them in a clear and engaging way, and yet ๐ป๐ฒ๐๐ฒ๐ฟ ๐๐ฎ๐ฐ๐ฟ๐ถ๐ณ๐ถ๐ฐ๐ฒ๐ ๐ฟ๐ถ๐ด๐ผ๐ฟ. The mathematical foundations are always there โ equations included โ but introduced only when they are truly needed.
The book starts by revisiting just enough linear algebra, calculus, and probability to get you going, and then gradually builds up concepts on a need-to-know basis. Fundamental regression and classification methods (linear and logistic regression, SVMs, k-NN) are explained intuitively, before moving on to ๐ป๐ฒ๐๐ฟ๐ฎ๐น ๐ป๐ฒ๐๐๐ผ๐ฟ๐ธ๐, ๐๐ก๐ก๐, ๐ฎ๐ป๐ฑ ๐ฅ๐ก๐ก๐. I particularly enjoyed the treatment of gated recurrent units: the explanation of how a ๐๐ฅ๐จ learns to read, write, and erase memory โ complete with the governing equations โ was crystal clear.
What also stood out to me is how pragmatic the book is. Beyond algorithms, it walks through the real steps of an ML project: feature engineering, model selection, hyperparameter tuning, and the ever-present biasโvariance tradeoff, which is discussed thoughtfully throughout. The sections on ensemble methods, especially bagging and gradient boosting presented side by side, were especially well done.
I also appreciated how the book clears up several modern confusions. It rigorously defines โshallow learningโ as learning directly from features, reminding us that deep learning is not only about neural networks. It clarifies that embeddings donโt necessarily live in higher-dimensional spaces โ a misconception reinforced by todayโs focus on LLMs. And it shows how encoders and decoders go far beyond transformers, with practical examples using autoencoders and ladder networks for problems like limited labels or sparse recommender data.
Finally, the learning-to-rank section โ and in particular the discussion of ๐๐ฎ๐บ๐ฏ๐ฑ๐ฎ๐ ๐๐ฅ๐ง โ really stood out to me. Although introduced in the context of information retrieval and ranking documents returned by a search engine, the ideas immediately generalize beyond search. I couldnโt help but think about applications in logistics learning to sequence stops, prioritize actions, or even approximate solutions to ๐ฟ๐ผ๐๐๐ถ๐ป๐ด ๐ฝ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ๐ like VRP or TSP. Itโs one of those sections that doesnโt just explain a method โ it actively expands how you think about where that method can be used.
I highly recommend this book to anyone who wants to start machine learning on solid footing. Itโs equally valuable for seasoned practitioners like myself as a way to periodically step back, reconnect the dots, and remind ourselves what the field is really built on.
