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雷达卡
by Steven Finlay (Author)
About the Author
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.
About this book
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.
This third edition has been substantially revised and updated. It contains several new chapters and covers a broader set of topics than before, but retains the no-nonsense style of the original.
Table of contents
1. Introduction
2. What are Machine Learning and Artificial Intelligence (AI)?
3. What Do the Scores Generated by a Predictive Model Represent?
4. Why Use Machine Learning? What Value Does it Add?
5. How Does Machine Learning Work?
6. Using a Predictive Model to Make Decisions
7. That’s Scorecards, but What About Decision Trees?
8. Neural Networks and Deep Learning
9. Unsupervised and Reinforcement Learning
10. How to Build a Predictive Model
11. Operationalizing Machine Learning
12. The Relationship Between Big Data and Machine Learning
13. Ethics, Law and the GDPR
14. The Cutting Edge of Machine Learning
15. When Can I Buy a Self-Driving Car?
16. Concluding Remarks
Appendix A. Evaluating Predictive Models
Appendix B. Further Information and Recommended Reading
Appendix C. Popular Terms in Machine Learning and AI
Appendix D. A Checklist for Business Success
Length: 192 pages
Publisher: Relativistic; 3 edition (July 1, 2018)
Language: English
ISBN-10: 1999730348
ISBN-13: 978-1999730345
Relativistic__Artificial Intelligence and Machine Learning for Business_ A No-No.mobi
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