The book is structured into three main parts: , Systems , and Challenges . 1. Methods (The "How")
This section covers the core algorithms that power automation.
Because it is a collection of academic papers, it lacks a cohesive tutorial-style narrative; there are no step-by-step business implementation guides. Frank Hutter Lars Kotthoff Joaquin Vanschoren Editors Download Book Automated Machine Learning pdf
Focuses on "learning to learn," where systems use past experience from other datasets to speed up new tasks.
The book reviews mature open-source tools that implement these methods: The book is structured into three main parts:
A robust system based on the popular scikit-learn library that uses meta-learning to warm-start optimization. 3. Challenges (The Evaluation)
It details the series, which evaluates how well these automated systems perform against human experts on "blind" datasets. Critical Reception Because it is a collection of academic papers,
Explores how to automatically find the best settings (like learning rates) for models. It contrasts efficient Bayesian Optimization against traditional grid or random searches.