Artificial Intelligence, or AI, is no doubt one of the hottest buzz words at the moment. AI has penetrated into many aspects of our lives. To know AI and to be able to use AI will bring enormous benefits to our work and life. However, to learn AI is a daunting task for many people, largely due to the complex mathematics and sophisticated coding. This book aims to demystify AI and teach readers about AI from scratch, by using simple, plain, languages and simple, illustrative, code examples. This book is divided into three parts.

In part one, readers will follow an easy to read introduction about AI, including the history, the types of AI, the current status and possible future trend. It then introduces Python, A widely used programming tool for AI. In part two, we introduce Machine Learning aspect of AI, topics include classifications, regressions, and Clustering. It also includes the most popular Reinforcement Learning. In part three, it introduces Deep Learning aspect of AI, topics include Image Classifications, Transfer Learning, Recurrent Neural Network, and the latest Generative Adversarial Networks. It also includes the state of the art of GPU, TPU, cloud computing and edge computing.

This book is packed with interesting and exciting examples such as pattern recognitions, image classifications, face recognition (most controversial), age and gender detection, voice/speech recognition, chatbot, natural language processing, translation, sentiment analysis, predictive maintenance, finance and stock price analysis, sales prediction, customer segmentation, biomedical data analysis and much more.

PERRY XIAO, PhD, is Professor and Course Director of London South Bank University. He holds his doctorate in photophysics and is Director and co-Founder of Biox Systems Ltd., a university spin-out company that designs and manufactures the AquaFlux and Epsilon Permittivity Imaging system.

Part I - Introduction
Chapter 1: Introduction to AI, Machine Learning and Deep Learning
Chapter 2: Introduction to MATLAB and Python
Part II - Machine Learning
Chapter 3: Supervised Learning - Classifications
Chapter 4: Supervised Learning - Regressions 
Chapter 5: Unsupervised Learning - Clustering, PCA, and LDA 
Chapter 6: Semi-supervised Learning, Reinforecement Learning and Machine Learning Applications
Part III - Deep Learning
Chapter 7: Deep Learning  - CNN, AlexNet, and GoogLeNet
Chapter 8: Deep Learning - RNN and LSTM
Chapter 9: Deep Learning - GAN
Chapter 10: Deep Learning - GPI, TPU, Cloud Computing and Edge Computing 
Appendix A: MATLAB for AI Cheat Sheets
Appendix B: Python for AI Cheat Sheets