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 (AI), Machine Learning (ML), and Data Science (DS), 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. This book, firstly, was written for those who do not know mathematics and the theory of control, secondly, for those who tried the open-source approach but had difficulties. This book consists of three parts. Part 1 briefly introduces the history of Artificial Intelligence, Machine Learning, and Deep Learning, as well as algorithms. Part 2 introduces Azure Machine Learning, how to use it, and some examples as needed. Part 3 introduced visualization and Power BI, which is a visualization tool