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Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition Paperback – September 20, 2017
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About the Author
Sebastian Raschka, author of the bestselling book, Python Machine Learning, has many years of experience with coding in Python, and he has given several seminars on the practical applications of data science, machine learning, and deep learning, including a machine learning tutorial at SciPy - the leading conference for scientific computing in Python.
While Sebastian's academic research projects are mainly centered around problem-solving in computational biology, he loves to write and talk about data science, machine learning, and Python in general, and he is motivated to help people develop data-driven solutions without necessarily requiring a machine learning background.
His work and contributions have recently been recognized by the departmental outstanding graduate student award 2016-2017, as well as the ACM Computing Reviews' Best of 2016 award. In his free time, Sebastian loves to contribute to open source projects, and the methods that he has implemented are now successfully used in machine learning competitions, such as Kaggle.
Vahid Mirjalili obtained his PhD in mechanical engineering working on novel methods for large-scale, computational simulations of molecular structures. Currently, he is focusing his research efforts on applications of machine learning in various computer vision projects at the Department of Computer Science and Engineering at Michigan State University.
Vahid picked Python as his number-one choice of programming language, and throughout his academic and research career he has gained tremendous experience with coding in Python. He taught Python programming to the engineering class at Michigan State University, which gave him a chance to help students understand different data structures and develop efficient code in Python.
While Vahid's broad research interests focus on deep learning and computer vision applications, he is especially interested in leveraging deep learning techniques to extend privacy in biometric data such as face images so that information is not revealed beyond what users intend to reveal. Furthermore, he also collaborates with a team of engineers working on self-driving cars, where he designs neural network models for the fusion of multispectral images for pedestrian detection.
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Top customer reviews
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This is the best book I've seen for professional software engineers to bootstrap themselves into Data Science, Machine Learning and (with the 2nd ed) Deep Learning. It makes heavy use of the scikit-learn library; and the latter chapters give an excellent high-level overview of TensorFlow. Books in this space can often feel either too basic or too academic. Not this one -- for me it hits the sweet spot of explaining and doing.
What I love about Raschka's writing is how he builds up from theory to practical code. It lays out the concepts, math, and code together which helps comprehension. So, if you happen to be rusty in math, like me, you can look to the code to help explain what the equations actually do. The chapters of the book build up from each other; so many of the examples feel like they can be used as recipes for building your own custom models.
This 2nd edition added further explanations and clarifications to the 1st addition, together with added chapters for two widely-used deep learning algorithms of CNN (for image processing) and RNN (for language translation) using TensorFlow.
This book is one of the few machine learning books currently available in the market that provide fully-integrated, fully-working Python implementation codes. The author successfully made tremendous efforts in bringing a variety of sophisticated machine learning algorithms in both classical statistical learning and deep learning by simple, straightforward and clear explanation together with fully-working step-by-step python codes down to average readers with basic technical understanding in machine learning area.
This book could be a best fit to students and industry people who are interested in practical implementation and application of a variety of machine learning algorithms.
The clever choice of presenting applied case studies hand in hand with the corresponding ML math using python adds a lot of value to the book; this allows one to deep dive into pandas, matplotlib and sklearn. The last few chapters lay a strong foundation in neural networks model building and deep learning using google's tensorflow API.
Full disclosure: I already own the 1st edition of this book and I received an early draft copy of the 2nd edition. I volunteered to review the last few chapters of the book.
First, there is a framing effect, or lack thereof. Whereas the first time, I had had the "warm up" of reading worse Packt books before I read Version 1 - and the book looked great compared to what came before it - this time I *start* from Version 2, and realize that it is a good Packt book, but a Packt book nonetheless, i.e. something half-baked, scratchpad'y and low-value-added.
Second, I feel that the book's average level dipped with the addition of a co-author and a 200-page, 5-chapter block on artificial neural networks. (These 200 pages, using TensorFlow, replaced 70 pages of ANN coverage, relying on Theano, in Version 1. This is 95% of the book's change from the first edition: the non-ANN Chapters 1-11 grew slightly, by 20-plus pages. If ANNs - currently hyped as "deep learning" - are not your thing, you can save money and go with Version 1). I am new to TensorFlow, and I read pages 424-433 carefully. I did not enjoy it, and decided to order "Hands-on Machine Learning with Scikit-Learn and TensorFlow" by Geron, published by O'Reilly.
So here we are, with "It's okay". My advice to budding data scientists would be: use this book, but only for code samples. Get a proper book, like "Introduction to statistical learning" by James et al. or "Elements of Statistical Learning" by Hastie and Tibshirani, to understand the methods.