Publisher's Synopsis
Welcome, Machine Learning strategies are oriented to support business decision making. With well-designed models and adequate data, you can literally predict the future (keep in mind that there are futures that can be predicted with Machine Learning and others that cannot), gain competitive advantage, and make innovations. And if the above seems too little, with Machine Learning strategies you can know the behavior of clients or competitors, and therefore, anticipate their movements.Remember that: Machine learning is magical. You give it data and it manages to classify that data. For many, it can actually outperform a human! But, like so many problems in the world, there is a significant "garbage in, garbage out" aspect to machine learning. If the data you give it is trash, the learning algorithm is unlikely to be able to overcome it ( Daumé III, 2014, pag. 51)In this sense, by reading this book and applying its steps, they can learn about Machine Learning in the field of supervised learning. In this way, being a book aimed at entrepreneurs, administrators and innovators.In this way, the book presents Machine Learning models with simple data that is easy to understand. Each example contains specific elements for understanding those who do not want to know anything about the complex mathematics behind the Machine Learning models. Likewise, specific elements are also presented for those who are curious to know what "moves" or how it moves or what are the mechanisms with which Machine Learning performs its wonderful processes. In other words, if we place ourselves in a university environment, the book may be in the hands of a student or professor of an administration program or an engineering program. Although I love mathematics because of my profession as an engineer, I must mention that with the book you can learn Machine Learning without the need to strictly approach the mathematics..it presents an approach with practical examples supported by the software Orange (University of Ljubljana).
