What Every Engineer Should Know About Data-Driven Analytics

This book provides a comprehensive guide to building models that exploit algorithms to extract insights and make decisions from data. It covers predictive models built through machine learning, the augmentation of technical and mathematical materials with explanatory worked examples, and includes a glossary, lecture notes, self-assessments, and worked-out practice exercises.

Format: Paperback / softback
Length: 260 pages
Publication date: 13 April 2023
Publisher: Taylor & Francis Ltd

The book "Data Mining: Practical Machine Learning Tools and Techniques" is a comprehensive guide that provides readers with practical tools and techniques for extracting valuable insights from data. It utilizes case studies from various disciplines and sectors within engineering and other related technical areas to demonstrate how to go from data to insight and to decision-making.

The book begins by introducing various approaches to building models that exploit different algorithms, such as regression, classification, and clustering. It discusses predictive models that can be built through machine learning and used to mine patterns from large datasets. The book also explores the augmentation of technical and mathematical materials with explanatory worked examples, making it easier for readers to understand complex concepts.

In addition, the book includes a glossary, lecture notes, self-assessments, and worked-out practice exercises to help readers reinforce their understanding of the topics covered. Whether you are a data scientist, engineer, or business professional, this book will provide you with the skills and knowledge needed to effectively mine and analyze data to make informed decisions.

Overall, "Data Mining: Practical Machine Learning Tools and Techniques" is an essential resource for anyone interested in leveraging data to drive business growth and innovation.

Weight: 442g
Dimension: 155 x 234 x 19 (mm)
ISBN-13: 9781032235400