Machine Learning for Managers

Name: Machine Learning for Managers
Brand: Taylor & Francis
SKU: 9781032362434
Price: 341.0 NZD
Availability: InStock

By Paul Geertsema

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Summary

Machine Learning for Managers demystifies machine learning for those without a technical background. It explains in clear, accessible language what machine learning is, how it functions, and how managers can lead machine learning projects effectively. The book offers practical insights for integrating machine learning to improve decision-making, automate tasks, and boost business operations in a digital world.
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Format: Hardback

$341⁰⁰

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Machine Learning for Managers

$341⁰⁰

Book Hero Magic created this recommendation. While it's new and still learning, it may not be perfect - your feedback is welcome! IS THIS YOUR NEXT READ?

Ideal for business managers and entrepreneurs without a computer science or statistical background who want to understand and implement machine learning in their organisations. Suitable for readers seeking practical guidance on managing machine learning projects to create real-world business value.

This book is for managers who have been afraid of machine learning but want to understand it. It helps managers understand how machine learning works, what it can do and how it can be used to create value in the context of wider organisation. It will appeal to managers who want to learn more about machine learning applications in business.

Description

Machine Learning for Managers 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 organisation.

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

Book Hero Magic summarised reviews for this book. While it's new and still learning, it may not be perfect - your feedback is welcome! HOW HAS THIS BEEN REVIEWED?

Experts praise this book as a comprehensive yet non-technical introduction to machine learning, ideal for managers eager to harness AI in business. The engaging and light-hearted style makes a complex topic approachable, balancing technical terminology and management guidance. Reviewers commend its clarity, practical examples, and usefulness for bridging the gap between technical experts and managers.

Book Details

INFORMATION

ISBN: 9781032362434

Publisher: Taylor & Francis Ltd

Format: Hardback

Date Published: 19 June 2023

Country: United Kingdom

Imprint: Routledge

Illustration: 4 Tables, black and white; 41 Line drawings, black and white; 15 Halftones, black and white; 56 Illustrations, black and white

Audience: Tertiary education, Professional and scholarly

DIMENSIONS

Width: 156.0mm

Height: 234.0mm

Weight: 490g

Pages: 160

Collections

Business & Entrepreneurship Computing & Technology Education & Reference Non-Fiction Paul Geertsema

About the Author

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.