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Machine Learning Applications Using Python

Users: 1 - Average Rating: 5.00


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

Type:
Book

Area:
Machine Learning

Target Group:
Basic

DOI:
https://doi.org/10.1007/978-1-4842-3787-8


Author of the review:
Spiros Sirmakessis
University of Peloponnese


Reviews

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Pablo Guerrero-Garcia


The perfect book for anyone to be able to brag about doing machine learning in any high education grade, because although each field is different (and hence each software solution should also be different), the underlying machine learning approach does not differ much. The author proves this fact by considering medicine (healthcare), commerce (shopping) and economics (finance) in sequence; he seems to provide access to all Python codes via registration in his web page but the link he provides in the book does not work as of the end of March 2023 (namely the date of this comment).