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Artificial intelligence and machine learning for business / Steven Finlay

By: Material type: TextTextPublication details: Relativistic, 2018 Great Britain :Edition: 3rd edDescription: X, 182 p. ; 22 cmISBN:
  • 9781999730345
Other title:
  • Artificial intelligence and machine learning for business: A no-nonsense guide to data driven technologies
Subject(s): LOC classification:
  • TA 347 F56A 2018
Contents:
1. Introduction page 1-5 -- 2. What are machine learning and artificial intelligence (AI)? page 6-18 -- 3. What do the scores generated by a predictive model represent? page 19-25 -- 4. Why use machine learning? What value does it add? page 26-30 -- 5. How does machine learning work? page 31-41 -- 6. Using a predictive model to make decisions page 42-46 -- 7. That's scorecards, but what about decision trees? page 47-52 -- 8. Neural networks and deep learning page 53-63 -- 9. Unsupervised and reinforcement learning page 64-76 -- 10. How to build a predictive model page 77-92 -- 11. Operationalizing machine learning page 93-104 -- 12. The relationship between big data and machine learning page 105-110 -- 13. Ethical, law and the GDPR page 111-124 -- 14. The cutting edge of machine learning page 125-133 -- 15. When can i buy a self-driving car? page 134-141 -- 16. Concluding remarks page 142
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Holdings
Item type Current library Collection Shelving location Call number Status Date due Barcode Item holds
General Book General Book SPU Library, Chonburi campus General Books (ENGLISH) Floor 3: General Shelves (FOREIGN LANGUAGE) TA 347 F56A 2018 (Browse shelf(Opens below)) Available B009075
Total holds: 0

1. Introduction page 1-5 -- 2. What are machine learning and artificial intelligence (AI)? page 6-18 -- 3. What do the scores generated by a predictive model represent? page 19-25 -- 4. Why use machine learning? What value does it add? page 26-30 -- 5. How does machine learning work? page 31-41 -- 6. Using a predictive model to make decisions page 42-46 -- 7. That's scorecards, but what about decision trees? page 47-52 -- 8. Neural networks and deep learning page 53-63 -- 9. Unsupervised and reinforcement learning page 64-76 -- 10. How to build a predictive model page 77-92 -- 11. Operationalizing machine learning page 93-104 -- 12. The relationship between big data and machine learning page 105-110 -- 13. Ethical, law and the GDPR page 111-124 -- 14. The cutting edge of machine learning page 125-133 -- 15. When can i buy a self-driving car? page 134-141 -- 16. Concluding remarks page 142

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