This set of lecture notes, written for those who are unfamiliar with mathematics and programming, introduces the reader to important concepts in the field of machine learning. It consists of three parts. The first is an overview of the history of artificial intelligence, machine learning, and data science, and also includes case studies of well-known AI systems. The second is a step-by-step introduction to Azure Machine Learning, with examples provided. The third is an explanation of the techniques and methods used in data visualization with R, which can be used to communicate the results collected by the AI systems when they are analyzed statistically. Practice questions are provided throughout the book.

Part I Artificial Intelligence

Chapter 1 Summary of Artificial Intelligence

Chapter 2 Machine Learning

Chapter 3 Deep Learning

Chapter 4 Case Study

Chapter 5 Microsoft Azure Machine Learning Studio

Chapter 6 Create Prediction Model using Microsoft Azure Machine Learning Studio

Chapter 7 Create Prediction Models using Microsoft Azure Machine Learning Studio Web Service Deployment

Chapter 8 Create Prediction Model using Microsoft Azure Machine Learning Studio Script Integration

Part II Exercises

Chapter 9 Exercises

Part II Visualization

Chapter 10 Visualization

Chapter 11 Visualization with Power BI

Chapter 12 Visualization with R in Power BI

Bibliography