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Introduction to Machine Learning with Python: A Guide for Data Scientists 1st Edition
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About the Author
Andreas Müller received his PhD in machine learning from the University of Bonn. After working as a machine learning researcher on computer vision applications at Amazon for a year, he recently joined the Center for Data Science at the New York University. In the last four years, he has been maintainer and one of the core contributor of scikit-learn, a machine learning toolkit widely used in industry and academia, and author and contributor to several other widely used machine learning packages. His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms.
Sarah is a data scientist who has spent a lot of time working in start-ups. She loves Python, machine learning, large quantities of data, and the tech world. She is an accomplished conference speaker, currently resides in New York City, and attended the University of Michigan for grad school.
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Top Customer Reviews
I have a background in math and wrote software professionally for a number of years, but haven't spent much time doing either for the past 5-10 years. This book is technical enough to keep me interested, and accessible enough to allow me to ramp up on the language and the scikit framework.
An added bonus - the instructions actually allowed me to set up my development environment, and the code in the book actually runs!
100% recommend for someone looking to get started in ML with Python.
All the concepts mentioned here are heavily backed with well thought of and well presented figures, in such a way that again I'd suggest you don't even need python to understand. If you do know python, loading the data sets and reproducing the figures is just a few lines of easy to understand code away (with the exception of the mglearn library includes which does some "plotting magic" for you. However, I believe each of them were appropriate. You can ignore them and make the plots in your own way, or just print the variables, it just may not look as publication friendly).
Normally, I hesitate purchasing books that claim they may explain algorithms without the need of equations, and I expect them rather to be cook books of lightly and disjointly explained techniques (like an encyclopedia). However, I do not think such is true of this book. The power of scikit-learn is demonstrated and the algorithms behind them explained intuitively, and are referred as to how they fit together and complement each other.
As with any introductory read, a supplement is needed from time to time and the authors' reference to Elements of Statistical Learning is a useful one (equation heavy). There are points in the book where the author defers to elements of statistical learning. I found these points suitable since further explanation would be out of scope.
I read this book on my free time while on vacation, and much of the time I didn't have access to a computer. The concepts were so well presented that it was just a nice leisurely read. When I finally had time to access a computer, I was able to try the techniques on my data sets with some browsing back and forth through the book again, but otherwise with little effort.
Finally, since I myself am a researcher, I would recommend this book to any other researcher willing to start delving into the world of machine learning. Further reading will always be necessary, but this book will give you such a good intuitive understanding and overview of the subject matter that you'll know what to do to proceed next, and how to do it without running in circles. Even better, you'll likely already have applied it to your research!
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.
Most Recent Customer Reviews
Covers many of the important algorithms and necessary/required steps for Machine Learning, and does that in an approachable way.Read more