000			aam a22 4500
999			_c194017
003			SPU
005			20190824162139.0
008			190824b xxu||||| |||| 00| 0 eng d
020			_a9781999730345 _c1940
040			_aSPU
049			_aSPU_CHN
050			_aTA 347 _bF56A 2018
100			_9234632 _aFinlay, Steven
245			_aArtificial intelligence and machine learning for business / _cSteven Finlay
246			_aArtificial intelligence and machine learning for business: A no-nonsense guide to data driven technologies
250			_a3rd ed.
260			_bRelativistic, _c2018 _aGreat Britain :
300			_aX, 182 p. ; _c22 cm.
505			_a1. 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
650	0	0	_947448 _aNEURAL NETWORKS (COMPUTER SCIENCE)
650	0	0	_954523 _aMACHINE LEARNING
650	0	0	_9212924 _aBIG DATA
650	0	0	_938892 _aARTIFICIAL INTELLIGENCE
650	0	0	_939148 _aARTIFICIAL INTELLIGENCE _xDATA PROCESSING
850			_aSPU
942			_2lcc _cGEN
998			_amonthira 240819 _bmonthira 240819