Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies | 誠品線上

Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies

內容簡介

內容簡介 Artificial Intelligence (AI) and Machine Learning are now mainstream business tools. They are being applied across many industries to increase profits, reduce costs, save lives and improve customer experiences. Organizations which understand these tools and know how to use them are benefiting at the expense of their rivals. Artificial Intelligence and Machine Learning for Business cuts through the hype and technical jargon that is often associated with these subjects. It delivers a simple and concise introduction for managers and business people. The focus is very much on practical application and how to work with technical specialists (data scientists) to maximize the benefits of these technologies. Steven Finlay is a data scientist with more than 20 years' experience of developing practical machine learning-based solutions. He holds a PhD in management science and is an honorary research fellow at Lancaster University in the UK. He is currently Head of Analytics for Computershare Loan Services (CLS) in the UK. Dr Finlay has published a number of practically focused books about machine learning, artificial intelligence and financial services. Please note that the new, fourth, edition of this book is now available.

作者介紹

作者介紹 Steven Finlay is a data scientist with more than 20 years' experience of developing practical "value add" machine learning solutions in large scale data environments. He holds a PhD in predictive modelling and is an honorary research fellow at Lancaster University in the UK. He is currently Head of Analytics for Computershare Loan Services (CLS) in the UK. Dr Finlay has published a number of practically focused books about machine learning, artificial intelligence and financial services. His other books include: Predictive Analytics in 56 Minutes. Predictive Analytics, Data Mining and Big Data. Myths, Misconceptions and Methods. Credit Scoring, Response Modeling and Insurance Rating. A Practical Guide to Forecasting Consumer Behavior. The Management of Consumer Credit. Theory and Practice. Consumer Credit Fundamentals.

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