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Introduction to Machine Learning with Python: A Guide for Data Scientists 1st Edition, Kindle Edition
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|Length: 396 pages|
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Most helpful customer reviews on Amazon.com
A clear writer/speaker - Very good, look forward to his next book(s)
The book starts with only four sentences about the Jupyter notebook although is the main environment for the whole book. The first code sample shown starts on line two of a cell, and it was very strange there was no line one. I was wondering if there was some type of misprinting.
The code as printed is broken on page 10 where there is a line with 'display(data_pandas)'. This line gave me an error that display was unrecognized. I thought maybe this was a built-in Jupyter function so I went online to search. Eventually, I had to go to the author's GitHub and ask about this problem where I was told that he simply forgot to include 'from IPython.display import display'. It was a surprising admission because he did not say there was a misprint or mistake, but simply that he forgot to do that. It is very obvious there were zero technical reviewers for this book, because they would have also noticed the broken code right away.
On page 11 we are introduced to a library called 'mglearn' which is a utility function that authors say they wrote for the book. Strangely, this repository has 733 stars on GitHub so it is obvious the library is not just for the book. Then in chapter two the author has tons of calls to mglearn which take in multiple parameters. The parameters are never explained and you have to go to the author's GitHub to see what the code actually does. In the 2nd chapter multiple of these mglearn calls broke for me. One seemed to be a conflict with numpy, and another I never figured out. I went to look at dicussions on mglearn to discover it is still a work in progress and there were sections where somebody was notifying the author that something was broken, and the author replying that he would look at it soon.
The second chapter has 120 cell entries for supervised learning techniques. Each cell has roughly 5-10 lines of code, so there are nearly 1000 lines of code for the second chapter and they are all tossed into one gigantic Jupyter notebook. Explanations are very weak often defaulting to a brief description followed by code and then more code. Function calls and parameters are rarely explained at all.
The last chapter is about natural language processing which is the machine learning subject I am most familiar with. Terms are often introduced with zero effort to define them, and it is assumed you already know many of the concepts. TF-IDF barely had any explanation at all, except to show the forumla for it. You can find much better explanations online.
For a book which is so heavy on code and light on explanations, it is unacceptable that the code is broken.