Machine Learning Applications Using Python

Cases Studies from Healthcare, Retail, and Finance

  • Puneet Mathur

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Puneet Mathur
    Pages 77-119
  3. Puneet Mathur
    Pages 135-145
  4. Puneet Mathur
    Pages 147-157
  5. Puneet Mathur
    Pages 159-181
  6. Puneet Mathur
    Pages 183-216
  7. Puneet Mathur
    Pages 217-237
  8. Puneet Mathur
    Pages 249-257
  9. Puneet Mathur
    Pages 259-270
  10. Puneet Mathur
    Pages 271-293
  11. Puneet Mathur
    Pages 295-324
  12. Puneet Mathur
    Pages 325-354
  13. Puneet Mathur
    Pages 363-372
  14. Back Matter
    Pages 373-379

About this book


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. 

You will:
  • 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


Machine Learning Python Healthcare Finance Retail

Authors and affiliations

  • Puneet Mathur
    • 1
  1. 1.BangaloreIndia

Bibliographic information