
Demystifying AI by Dothang Truong
As artificial intelligence advances at an exponential pace, understanding data science and machine learning has become increasingly essential. Yet, the wide range of available resources can be daunting, posing challenges for beginners. This second book builds on the foundation laid in the first, Data Science and Machine Learning for Non-Programmers: Using SAS Enterprise Miner, providing similar fundamental knowledge of data science and machine learning in an accessible way. It is specifically designed to cater to readers who prefer a hands-on guide using IBM SPSS Modeler, a widely popular software that does not require coding or programming skills. Like the first book, this volume helps learners from various non-technical fields gain practical insight into machine learning but shifts the focus to a different tool for those seeking alternatives to coding.
In this book, readers are guided through practical implementations using real datasets and IBM SPSS Modeler, a user-friendly data mining tool. The approach remains consistent with a focus on application, providing step-by-step instructions for all stages of the data mining process using two large datasets, ensuring continuity and reinforcing concepts in a cohesive project framework. This book also offers practical advice on presenting data mining results effectively, aiding readers in communicating insights clearly to stakeholders.
Together with the first book, this volume is a companion for beginners and experienced practitioners alike. It targets a broad audience, including students, lecturers, researchers, and industry professionals. It offers flexibility in learning pathways and deepens understanding of data science using easy-to-follow, software-based approaches.
-
Data Mining with R
-
Biological Data Mining
-
Exploratory Data Analysis Using R
-
Advanced Data Science and Analytics with Python
-
Automated Data Analysis Using Excel
-
Privacy-Aware Knowledge Discovery
-
Computational Intelligent Data Analysis for Sustainable Development
-
Data Mining for Design and Marketing
-
Data Classification
-
Statistical Data Mining Using SAS Applications
-
Mining Software Specifications
-
Contrast Data Mining
-
Introduction to Computational Health Informatics
-
Social Networks with Rich Edge Semantics
-
Graph-Based Social Media Analysis
-
Industrial Applications of Machine Learning
-
Computational Business Analytics
-
Event Mining
-
Advances in Machine Learning and Data Mining for Astronomy
-
Human Capital Systems, Analytics, and Data Mining
-
Practical Graph Mining with R
-
Large-Scale Machine Learning in the Earth Sciences
-
Support Vector Machines
-
Geographic Data Mining and Knowledge Discovery
-
Data Science and Machine Learning for Non-Programmers
-
Knowledge Discovery from Data Streams
-
Data Science and Analytics with Python
-
RapidMiner, Second Edition
-
Feature Engineering for Machine Learning and Data Analytics
-
Text Mining and Visualization
-
Knowledge Guided Machine Learning
-
Healthcare Data Analytics
-
Text Mining
Dothang Truong is the Associate Dean for the School of Graduate Studies and Professor of Aviation Data Science at Embry Riddle Aeronautical University, Daytona Beach, Florida. He has extensive teaching and research experience in machine learning, artificial intelligence, data analytics, aviation safety, air transportation management, and supply chain management. In 2022, Dr. Truong received the Frank Sorenson Award for the outstanding achievement of excellence in aviation research and scholarship. He is ranked #1 globally in the specialty of Traffic Management by ScholarGPS (2024) and recognized as a Highly Ranked Scholar—Prior Five Years, placing in the top 0.05% of scholars worldwide for exceptional scholarly productivity, impact, and quality.
| SKU | Unavailable |
| ISBN 13 | 9781032740003 |
| ISBN 10 | 1032740000 |
| Title | Demystifying AI |
| Author | Dothang Truong |
| Series | Chapman And Hall Crc Data Mining And Knowledge Discovery Series |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Taylor & Francis Ltd |
| Year published | 2025-11-11 |
| Number of pages | 600 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |
































