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.
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.
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
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
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