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 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 Learn Who This Book Is For Data scientists and machine learning professionals.
This book has some useful information but over half of it it’s just to be there. Pages with nothing useful, or should I say, a waste of time. The information should be kept in a compact form. I think I saw this part “I have removed the names deliberately to maintain confidentiality” for over 20 times in this book. How would I say this: we don’t care, just get to the point. As I said, it’s like this book was made to waste the time of the reader. Very little useful info for over 300 pages. If someone would rewrite this book, should be around 100-150 pages and just useful things, not repeating the same story on every 30 pages.
Actually, I was not expecting panacea for ML pipelines by reading this book. However, being massive 384 pages are spent for problem definitions that are loosely related to ML industry. The book as if written from conference speeches frim different topics, author has a very little knowledge how to write technical books with precise concepts. It has a lot of typos, meanigless explanations and, how author claims, ideas for monetizing ML, but without ML entirely.
In contrast, Puneed Mathur did a good job in analyzing datasets giving the key insights where to pay attention and draw conclusions. Anyway, even the statistical analysis was in the degree of school student who tries to explain unnecessary and obvious things just to fill pages with words.