Geertsema Paul. Machine Learning for Managers

Routledge, 2023. — 181 p. — ISBN: 9781032362427.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. Many excellent books 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 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.I. Understanding machine learning.
Let’s jump right in.
Different kinds of ML.
Creating ML models.
Linear models.
Neural networks.
Tree-based approaches, ensembles, and boosting.
Dimensionality reduction and clustering.
Unstructured data.
Explainable AI.
II. Managing machine learning projects.
The ML system lifecycle.
The big picture.
Creating value with ML.
Making the business case.
The ML pipeline.
Development.
Deployment and monitoring.