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Introduction to Machine Learning with Python: A Guide for Data Scientists
Free Download Introduction to Machine Learning with Python: A Guide for Data Scientists
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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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Product details
Paperback: 400 pages
Publisher: O'Reilly Media; 1 edition (October 21, 2016)
Language: English
ISBN-10: 1449369413
ISBN-13: 978-1449369415
Product Dimensions:
7 x 0.8 x 9.2 inches
Shipping Weight: 1.5 pounds (View shipping rates and policies)
Average Customer Review:
4.2 out of 5 stars
43 customer reviews
Amazon Best Sellers Rank:
#19,235 in Books (See Top 100 in Books)
Fantastic introduction to machine learning in Python. The examples are well written, and do a very nice job of introducing both the implementation and the concept for each model. I'm halfway thru the book, and am really enjoying it.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.
This book walks thru a TON of ML algorithms and applications with example code but the code is so succinct that it's not really a programming book as much as a crash course in some ML math libraries available for Python, what the algorithms do and when to use them. It doesn't get into the math but it does give clear examples and explanations of when to use each algorithm and how. It's all terribly practical and understandable. I'm a fan. Also btw I'm a computer programmer and ML novice... I'm not used to reading Python but it's simple enough if you know other languages.
The book is printed in black-and-white making it *really* hard to understand which classes / data points the authors are referring to.Nevertheless, this is a good intro book and a nice companion to online classes that do not provide written notes.
This is a great book, and I'd say it is even great for those that are not familiar with python (you just obviously won't be able to run the code). For anyone with some basic understanding of linear algebra/statistics, the authors are able to present to you all the important (and sometimes subtle but significant) details, without the usage of equations, and more importantly, how they all relate to one another.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!
A healthy discussion of the skills and techniques you'll need to perform best-practices machine learning and data science. Very concise code examples and practical demos!
I bought this book to help me get up and running quick for a project in an "Introduction to Machine Learning" independent study course. Of the books I bought for the same task, this was by far the most helpful for building practical machine learning applications.The book is a great introduction to the scikit-learn framework which, in my opinion, is an extremely elegant machine learning tool kit.Reading this book helped me improve the quality of the code I was developing for the project which dramatically improved the speed I could produce new results for the project.If you are looking for an extremely theoretical text on machine learning, then you might want to look elsewhere.If you are looking for a guided introduction to the "bread-and-butter tools" of a great machine learning framework in Python, buy this.
Good introduction to machine learning.
I've attended Andreas ODCS sessions, where he works thru the examples, and adds color commentary.A clear writer/speaker - Very good, look forward to his next book(s)
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