Artificial intelligence and machine learning for business /
Finlay, Steven
Artificial intelligence and machine learning for business / Artificial intelligence and machine learning for business: A no-nonsense guide to data driven technologies Steven Finlay - 3rd ed. - Great Britain : Relativistic, 2018 - X, 182 p. ; 22 cm.
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
9781999730345 1940
NEURAL NETWORKS (COMPUTER SCIENCE)
MACHINE LEARNING
BIG DATA
ARTIFICIAL INTELLIGENCE
ARTIFICIAL INTELLIGENCE--DATA PROCESSING
TA 347 / F56A 2018
Artificial intelligence and machine learning for business / Artificial intelligence and machine learning for business: A no-nonsense guide to data driven technologies Steven Finlay - 3rd ed. - Great Britain : Relativistic, 2018 - X, 182 p. ; 22 cm.
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
9781999730345 1940
NEURAL NETWORKS (COMPUTER SCIENCE)
MACHINE LEARNING
BIG DATA
ARTIFICIAL INTELLIGENCE
ARTIFICIAL INTELLIGENCE--DATA PROCESSING
TA 347 / F56A 2018