Machine learning for managers

Table of contents:
Part 1: Understanding Machine Learning 1. Let's jump right in 2. Different kinds of ML 3. Creating ML models 4. Linear models 5. Neural networks 6. Tree-based approaches, ensembles and boosting 7. Dimensionality reduction and clustering 8. Unstructured data 9. Explainable AI Part 2: Managing Machine Learning Projects 10. The ML system lifecycle 11. The big picture 12. Creating value with ML 13. Making the business case 14. The ML pipeline 15. Development 16. Deployment and monitoring

[https://www.routledge.com/Machine-Learning-for-Managers/Geertsema/p/book/9781032362427?srsltid=AfmBOooSJRtQzXKcnNVQVw2y4_P_OeCEw23HaLPpDvsyDiyF-cj5a69x]

Machine learning can help managers make better predictions, automate complex tasks and improve business operations. Managers who are familiar with machine learning 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.

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

(https://www.routledge.com/Machine-Learning-for-Managers/Geertsema/p/book/9781032362427?srsltid=AfmBOooSJRtQzXKcnNVQVw2y4_P_OeCEw23HaLPpDvsyDiyF-cj5a69x)