Machine Learning: A Non-Technical Introduction

Nowadays we often hear talk about Machine Learning, Big Data, Data Science and Deep Learning - concepts that are revolutionising many areas of our lives. It is therefore important that we understand them, even those of us who are not technicians in these areas, for nearly everyone must make decisions and evaluate strategies involving the use of these tools.

The aim of this book is to help even a novice approach these issues for the first time so as to understand their usefulness and limitations, and to develop the basic vocabulary necessary to address the concepts involved. The text is purposely presented at an introductory level to ensure its accessibility to all readers.

The first chapter deals with the concept of learning from a general point of view and introduces preliminary concepts of data analysis and statistics. The second chapter introduces the reader to words used by people working in the field of Machine Learning. The third and fourth chapters explain the concepts commonly used to classify Machine Learning techniques:
- supervised and unsupervised learning;
- formal and emerging learning.

Finally, the last chapter illustrates one of the techniques of Machine Learning: Self-Organising Maps.