The Hundred-Page Machine Learning Book

*The Hundred-Page Machine Learning Book* by Andriy Burkov offers a compact yet comprehensive overview of the machine learning field, designed to be read in a single sitting. Despite its brevity, the book covers a vast array of essential topics, ranging from classical methods like linear and logistic regression to modern techniques such as support vector machines, deep learning, boosting, and random forests. It balances theory and practice effectively, including necessary mathematical equations that other short guides often omit, while explaining core concepts in concise, accessible language. This manual serves as a practical "how-to" guide for data science, making it a valuable resource for both newcomers and experienced practitioners. It does not assume high-level mathematical training or prior programming experience, making the content accessible to a wide audience, including engineers and PhD students. The book also incorporates Python code examples to illustrate algorithms, leveraging one of the most popular languages in the industry. Praised by experts from Google, Amazon, and LinkedIn, the text cuts through the noise to provide a solid introduction to machine learning without requiring an enormous time investment. It functions as a foundational starting point, encouraging readers to use it as a reference while exploring specific methods further through its companion wiki. By condensing complex material into roughly 100 pages, Burkov provides a broad view of the field that is useful for academic study and day-to-day engineering work.