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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkit
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Today's book review is, "Hands-On Machine Learning with scikit-learn and Scientific Python Toolkit" by Tarek Amr.
Who is this book for:
This book is a really quick introduction to using scikit-learn as well as a variety of other packages such as Pandas and Matplotlib. The book is meant to just get the basic ideas and to write some code to implement them. This book would be good for a business analyst who has some basic coding knowledge.
Who is this book not for:
The book itself covers a variety of topics however it lacked details around what should be reviewed beyond the basic implementation of code and the explanations on how things work were too simple. More details around the concepts could make this book a win however it isn't for those serious about data science or machine learning.
While not a deal breaker in itself, the structure of the book seemed a bit odd to me. For example, the chapter on preparing your data came after decision trees and linear model. While decision trees can handle missing values, variable selection methods such as RFE are covered in this chapter. Variable selection is applicable to both decision trees and linear models.
Overall I felt the book had the bones of a good machine learning book however it lacked the meat to make machine learning and scikit-learn really useful.
Rating: 2/5 STARS
Buy Book Here (my affiliate link):
Quant t-shirts, mugs, and hoodies:
Connect with me:
☕ Show Your Support and Buy Me a Coffee ☕
Who is this book for:
This book is a really quick introduction to using scikit-learn as well as a variety of other packages such as Pandas and Matplotlib. The book is meant to just get the basic ideas and to write some code to implement them. This book would be good for a business analyst who has some basic coding knowledge.
Who is this book not for:
The book itself covers a variety of topics however it lacked details around what should be reviewed beyond the basic implementation of code and the explanations on how things work were too simple. More details around the concepts could make this book a win however it isn't for those serious about data science or machine learning.
While not a deal breaker in itself, the structure of the book seemed a bit odd to me. For example, the chapter on preparing your data came after decision trees and linear model. While decision trees can handle missing values, variable selection methods such as RFE are covered in this chapter. Variable selection is applicable to both decision trees and linear models.
Overall I felt the book had the bones of a good machine learning book however it lacked the meat to make machine learning and scikit-learn really useful.
Rating: 2/5 STARS
Buy Book Here (my affiliate link):
Quant t-shirts, mugs, and hoodies:
Connect with me:
☕ Show Your Support and Buy Me a Coffee ☕
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