
Machine Learning Applications Using Python: Cases Studies from Healthcare, Retail, and Finance
Puneet Mathur
Résumé
Machine Learning Applications Using Python is divided into three sections, one for each of the domains (healthcare, finance, and retail). Each section starts with an overview of machine learning and key technological advancements in that domain. You'll then learn more by using case studies on how organizations are changing the game in their chosen markets. This book has practical case studies with Python code and domain-specific innovative ideas for monetizing machine learning.
What You Will Learn
- Discover applied machine learning processes and principles
- Implement machine learning in areas of healthcare, finance, and retail
- Avoid the pitfalls of implementing applied machine learning
- Build Python machine learning examples in the three subject areas
Who This Book Is For
Data scientists and machine learning professionals.
Part 1 : HealthcareChapter 1. Overview of machine learning in healthcare.Chapter 2. Key technological advancements in healthcare.Chapter 3. How to implement machine learning in healthcare.Chapter 4. Case studies on how organizations are changing the game in the market.Chapter 5. Pitfalls to avoid while implementing machine learning in healthcare.Chapter 6. Healthcare specific innovative Ideas for monetizing machine learning.
Part 2: Retail Chapter 7. Overview of machine learning in Retail.Chapter 8. Key technological advancements in Retail.Chapter 9. How to implement machine learning in Retail.Chapter 10. Case studies on how organizations are changing the game in the market. c. One discussion based case study. d. One practical case study with Python code.Chapter 11. Pitfalls to avoid while implementing machine learning in retail.Chapter 12. Retail specific innovative Ideas for monetizing machine learning.
Part 3: Finance Chapter 13. Overview of machine learning in Finance.Chapter 14. Key technological advancements in Finance.Chapter 15. How to implement machine learning in Finance.Chapter 16. Case studies on how organizations are changing the game in the market. e. One discussion based case study. f. One practical case study with Python code.Chapter 17. Pitfalls to avoid while implementing machine learning in Finance.Chapter 18. Finance specific innovative Ideas for monetizing machine learning.
Caractéristiques techniques
PAPIER | |
Éditeur(s) | Apress |
Auteur(s) | Puneet Mathur |
Parution | 12/12/2018 |
Nb. de pages | 379 |
EAN13 | 9781484237861 |
Avantages Eyrolles.com
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