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The Hundred-page Machine Learning Book

A compact, practical field guide to the core ideas, algorithms, and workflow of modern machine learning, distilling decades of research into what actually matters in practice.

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What it’s about

The Hundred-Page Machine Learning Book condenses the vast body of machine learning knowledge into a lean, readable guide covering supervised, unsupervised, and other learning paradigms. Andriy Burkov walks readers from the mathematical notation and fundamental algorithms (linear and logistic regression, decision trees, SVM, kNN) through neural networks and deep learning, then into the practical craft of feature engineering, regularization, model assessment, and hyperparameter tuning. Rather than exhaustively cataloging every technique, it curates the parts with proven practical value and teaches readers how to think about whether a problem is 'machine-learnable' and which methods to try. It is ideal both for beginners seeking a comfortable grasp of the field and for experienced practitioners wanting a concise reference and a springboard for further self-improvement.

The through-line

Who it’s for
An aspiring or practicing data analyst / machine learning engineer who wants to build useful predictive models and understand the field enough to ask the right questions.
The problem
They need to solve practical problems by building statistical models from data, but the field is vast, mathematical, and scattered across thousand-page textbooks. They feel overwhelmed, unsure whether a problem is even 'machine-learnable' or which technique to try.
The plan
  1. Learn the types of learning and how supervised learning works end to end.
  2. Master the notation, math, and fundamental algorithms (regression, trees, SVM, kNN).
  3. Understand the anatomy of a learning algorithm and gradient descent.
  4. Apply basic practice: feature engineering, three sets, regularization, model assessment, hyperparameter tuning.
  5. Extend to neural networks, deep learning, and specialized problem/solution paradigms.
The payoff
The reader can frame practical problems as machine learning tasks, select appropriate algorithms, engineer features, and assess models rigorously. · They work confidently with libraries like scikit-learn and can diagnose and fix underfitting, overfitting, and imbalance.

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