Textbook in PDF format

This textbook presents basic knowledge and essential toolsets needed for people who want to step into artificial intelligence (AI). The book is especially suitable for those college students, graduate students, instructors, and IT hobbyists who have an engineering mindset. That is, it serves the idea of getting the job done quickly and neatly with an adequate understanding of why and how. It is designed to allow one to obtain a big picture for both AI and essential AI topics within the shortest amount of time.
The book and associated materials have been proven to be able to assist a student with limited or even no knowledge in coding, data analytics, and machine learning to develop a basic and competing ability to understand, communicate, and implement AI.
Preface.
Preparation Knowledge: Basics of AI.
Tools for Artificial Intelligence.
Linear Models.
Decision Trees.
Support Vector Machines.
Bayesian Algorithms.
Artificial Neural Networks.
Deep Learning.
Ensemble Learning.
Clustering.
Dimension Reduction.
Anomaly Detection.
Association Rule Learning.
Value-Based Reinforcement Learning.
Policy-Based Reinforcement Learning.
A Appendices.
Bibliography.
Index