Paul Geertsema

Machine Learning for Managers

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Routledge / Taylor & Francis
• Seitenzahl: 182
• Erscheinungstermin: 19. Juni 2023
• Englisch
• Abmessung: 234mm x 156mm x 10mm
• Gewicht: 453g
• ISBN-13: 9781032362427
• ISBN-10: 1032362421
• Artikelnr.: 67515394

Autorenporträt

Paul Geertsema is an academic and consultant in the areas of finance, data science and machine learning. His research involves the application of contemporary machine learning methods to solving problems in finance and business. He teaches Modern Investment Theory and Management (final-year undergraduate) and Financial Machine Learning (postgraduate) at the University of Auckland. Dr Geertsema has published in numerous international peer-reviewed journals, including the Journal of Accounting Research and the Journal of Banking and Finance, and serves on the board of the AI Researchers Association. Prior to his return to academia, Dr Geertsema worked at Barclays Capital as a derivatives trader in Hong Kong and as a sell-side research analyst in London.

Inhaltsangabe

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

Rezensionen

"If you are considering implementing machine learning in your business but don't know where to start, this is the right book for you. Machine Learning for Managers is a comprehensive but non-technical introduction to the topic with many relevant examples and implementation guidelines. The split into a detailed overview and project management instructions is ideal for readers who don't have the time to acquire programming skills but are passionate about leveraging AI to enhance business performance. The author's very engaging writing style makes reading a book about a potentially very dry topic enjoyable."

Christoph Schumacher, Professor of Innovation and Economics; Director Knowledge Exchange Hub, Massey University, New Zealand

"This book fills an important gap between pure-technical and pure-managerial descriptions of machine learning (ML). Written in a no-nonsense light-hearted style, it is easy to follow, yet doesn't shy away from using technical terms that are important for managers to be able to speak to their ML engineers. Highly recommended for managers looking to understand more about what is under the hood of ML."

Tava Olsen, Professor, Deputy Dean, Melbourne Business School



"Machine Learning for Managers is a safe haven for non-technical readers interested in understanding what AI and specifically ML is about. With clear, direct and witty language, Geertsema ensures that our journey into AI is like a walk in the park. It is easy, pleasurable and refreshing in its approach and powerful in its choice of illustrations. It brings to the forefront key concepts such as explainability, governance and business case making the message lucent and highly applicable to managers interested in incorporating ML into their business. As a practitioner focussed on human centric AI, I am particularly keen in bringing down AI/ML from its ivory tower status. This book is exactly a tool for this as it provides transparency, deciphers otherwise perceived complex language and is the basis for what ML should do best: to serve you. By far the best introductory ML roadmap I have come across. A must read."

Jose Romano, Senior Manager at the European Investment Fund and former Entrepreneur in Residence at TAZI.AI

"The two complementary parts of the book form a comprehensive and practical guide to machine learning. The first part provides a nontechnical overview of machine learning algorithms, demystifying the jargon in the field, which is crucial for students, lecturers and practitioners aiming to apply machine learning to resolve real-life business problems. The second part insightfully examines how machine learning outcomes can be developed and deployed in the organisation's processes. A recommended work for anyone looking to successfully manage the tsunami of big data!"

Leo Paas, Professor, The University of Auckland Business School; Program Director, Master of Business Analytics

"This book provides an outstanding introduction to machine learning from a management perspective. It gives a very clear presentation of the state-of-the-art machine learning methods and how to manage machine learning projects efficiently. It brings a fresh, unique focus on how to learn machine learning from a business perspective. It is highly practical and discusses in detail how a machine learning project should be deployed in real business applications. Not to be missed by any manager with a serious interest in AI and Machine Learning."

Albert Bifet, Professor, Director of the AI Institute, The University of Waikato, New Zealand