Artificial intelligence and machine learning for business / Steven Finlay
Material type:![Text](/opac-tmpl/lib/famfamfam/BK.png)
- 9781999730345
- Artificial intelligence and machine learning for business: A no-nonsense guide to data driven technologies
- TA 347 F56A 2018
Item type | Current library | Collection | Shelving location | Call number | Status | Date due | Barcode | Item holds | |
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SPU Library, Chonburi campus | General Books (ENGLISH) | Floor 3: General Shelves (FOREIGN LANGUAGE) | TA 347 F56A 2018 (Browse shelf(Opens below)) | Available | B009075 |
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TA 347.F5 ป451ฟ 2542 ไฟไนต์เอลิเมนต์ในงานวิศวกรรม / | TA 347.F5 ป451ฟ 2542 ไฟไนต์เอลิเมนต์ในงานวิศวกรรม / | TA 347.F5 ป451ฟ 2542 ไฟไนต์เอลิเมนต์ในงานวิศวกรรม / | TA 347 F56A 2018 Artificial intelligence and machine learning for business / | TA 351 ส657ส 2547 สถิติวิศวกรรม = | TA 351 B43V 2003 Vector mechanics for engineers : | TA 351 B43V 2003 Vector mechanics for engineers : |
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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