The book will cover the different algorithms used in machine learning according to its different types. We'll cover algorithms for supervised learning, unsupervised learning, and reinforcement learning. In other words we'll go over how machine learning is task driven (e.g. predicting the next value), data driven (e.g. identify and classify customer clusters), and is able to learn from its own mistakes.

We'll also get a bit technical-just slightly when we cover computational learning theory, big data, statistics, learning and optimization, Bayesian networks, support vector machines, genetic algorithms, and data mining. Again, we have tried to the best of our abilities to simplify these concepts for the lay man.

At the end of this book we have also recommended related AI technologies, open source tools, and programming languages. Well, that is if you are interested to learn how to actually develop this technology or to at least be able to understand its more technical features.

Needless to say, machine learning is a new and exciting field with a lot of beneficial applications. It facilitates more accurate medical diagnosis, it can simplify product marketing, create more accurate sales forecasts, improves the precision of many financial rules, simplifies documentation that is time intensive, fine tune predictive maintenance, and a host of other benefits.