Название: Machine Learning for Managers
Автор: Paul Geertsema
Издательство: Routledge
Год: 2023
Страниц: 181
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

Machine Learning can help managers make better predictions, automate complex tasks and improve business operations. Managers who are familiar with Machine Learning (ML) are better placed to navigate the increasingly digital world we live in. There is a view that Machine Learning is a highly technical subject that can only be understood by specialists. However, many of the ideas that underpin Machine Learning are straightforward and accessible to anyone with a bit of curiosity. This book is for managers who want to understand what Machine Learning is about, but who lack a technical background in Computer Science, statistics or math. The book describes in plain language what Machine Learning is and how it works. In addition, it explains how to manage Machine Learning projects within an organization.

Most books on Machine Learning (ML) and Artificial Intelligence (AI) fall into one of the two categories. Books in the first category are aimed at explaining the importance of AI to a general audience, including senior decision makers such as CEOs. There are many excellent books that explain how AI relates to corporate strategy, innovation and ethics, to name but a few. But after reading these books you still won’t know how ML actually works, or how to manage the implementation of an ML system.

Books in the second category are directed at academics or software engineers with specific technical backgrounds. The main thing you learn from reading these books is that you lack the required technical background. Again, many of these books are excellent – provided you have the right technical background.

Machine Learning for Managers covers the middle ground. It is complementary to the general interest and technical books, rather than a replacement for them. Machine Learning for Managers explains what ML is, how it works, how to get value from it and how to manage its implementation – in the real world.

This book should appeal to anyone that wants to learn more about using Machine Learning to drive value in real-world organizations.