Machine Learning Applications Using Python (eBook)
XVIII, 379 Seiten
Apress (Verlag)
978-1-4842-3787-8 (ISBN)
- 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
Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you'll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance, healthcare, and retail, but also work through practical case studies where machine learning has been implemented. 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 LearnDiscover applied machine learning processes and principlesImplement machine learning in areas of healthcare, finance, and retailAvoid the pitfalls of implementing applied machine learningBuild Python machine learning examples in the three subject areasWho This Book Is ForData scientists and machine learning professionals.
Puneet Mathur, MBA, PMP, CCD is a data scientist and machine learning consultant and alumni of IIM Bangalore in Business Analytics and Intelligence. He is a predictor and author of international bestsellers that teach people to predict in the right way. Throughout his career spanning 18 years, he has researched techniques of Predictive Analytics, Statistics and Machine Learning in relevant business domains.
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.
| Erscheint lt. Verlag | 12.12.2018 |
|---|---|
| Zusatzinfo | XVIII, 379 p. 80 illus. |
| Verlagsort | Berkeley |
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Finance • Healthcare • machine learning • Python • Retail |
| ISBN-10 | 1-4842-3787-0 / 1484237870 |
| ISBN-13 | 978-1-4842-3787-8 / 9781484237878 |
| Haben Sie eine Frage zum Produkt? |
PDF (Wasserzeichen)Größe: 7,2 MB
DRM: Digitales Wasserzeichen
Dieses eBook enthält ein digitales Wasserzeichen und ist damit für Sie personalisiert. Bei einer missbräuchlichen Weitergabe des eBooks an Dritte ist eine Rückverfolgung an die Quelle möglich.
Dateiformat: PDF (Portable Document Format)
Mit einem festen Seitenlayout eignet sich die PDF besonders für Fachbücher mit Spalten, Tabellen und Abbildungen. Eine PDF kann auf fast allen Geräten angezeigt werden, ist aber für kleine Displays (Smartphone, eReader) nur eingeschränkt geeignet.
Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen dafür einen PDF-Viewer - z.B. den Adobe Reader oder Adobe Digital Editions.
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen dafür einen PDF-Viewer - z.B. die kostenlose Adobe Digital Editions-App.
Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.
aus dem Bereich
