"Industrial Applications of Machine Learning" by Pedro Larrañaga, David Atienza, Javier Diaz-Rozo, Alberto Ogbechie, Carlos Esteban Puerto-Santana, and Concha Bielza is a book that provides a practical guide to the use of machine learning in industry. The book is aimed at engineers, data scientists, and other professionals who want to learn how to apply machine learning techniques to real-world problems.
The book covers a range of industrial applications of machine learning, including predictive maintenance, process optimization, quality control, fault detection, and anomaly detection. Each chapter provides a detailed overview of the problem and the data, as well as step-by-step instructions for implementing machine learning models in the industry.
One of the strengths of the book is its practical focus. The authors provide clear and concise explanations of the machine learning techniques used in each application, along with the Python code needed to implement them. The code is well-organized and easy to follow, making it easy for readers to adapt and customize the models for their own applications.
Another strength of the book is its emphasis on data preprocessing and feature engineering. The authors explain the importance of these steps in the machine learning process and provide practical guidance on how to perform them effectively. This is a valuable resource for professionals who are new to machine learning and want to learn how to work with messy or complex data.
Overall, "Industrial Applications of Machine Learning" is a great resource for professionals who want to learn how to apply machine learning to real-world problems in industry. The case studies are well-designed and cover a range of applications, making it a valuable reference for anyone interested in machine learning in industry.
