A must-read for understanding data warehousing, essential for building effective data pipelines in predictive maintenance.

This book provides practical insights into predictive maintenance strategies, focusing on real-world applications in manufacturing.

Ng's guide helps practitioners design machine learning systems effectively, crucial for creating robust predictive maintenance solutions.

Explore deep learning techniques tailored for time series data, enhancing your predictive maintenance capabilities with IoT data.

A practical guide to implementing machine learning algorithms, including Random Forest and Gradient Boosting, for predictive analytics.

This foundational book bridges the gap between data science concepts and business applications, essential for predictive maintenance.

Master the art of feature engineering, vital for improving model performance in predictive maintenance applications.

A practical approach to deploying machine learning applications effectively, crucial for your predictive maintenance projects.

A concise overview of data science principles, providing valuable insights into the data-driven decision-making process.

Silver's exploration of predictive analytics offers key lessons on improving accuracy in predictive maintenance.