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Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition, by Sebastian Raschka Vahid Mirjalili
PDF Download Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition, by Sebastian Raschka Vahid Mirjalili
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Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.
Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library.
Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world.
If you've read the first edition of this book, you'll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn.
Product details
Paperback: 622 pages
Publisher: Packt Publishing; 2nd edition (September 20, 2017)
Language: English
ISBN-10: 9781787125933
ISBN-13: 978-1787125933
ASIN: 1787125939
Product Dimensions:
7.5 x 1.4 x 9.2 inches
Shipping Weight: 2.4 pounds (View shipping rates and policies)
Average Customer Review:
4.2 out of 5 stars
48 customer reviews
Amazon Best Sellers Rank:
#18,308 in Books (See Top 100 in Books)
This book is excellent for the following demographic:People who already have a decent level of skill and experience in statistics who want to: - 1) Elevate their understanding of ML techniques without absolutely breaking their skull on dense theory - 2) Learn how to implement the algorithms in Python and gain moderate proficiency in sci-kit learnI would say it's not a beginner's book, but more for intermediates. I am half-way through and find it a little challenging, but definitely attainable. This balance I consider to be putting me right in the sweet spot for learning. To judge whether you're a good candidate for this book, you can compare your experience and skill to me :I started this book after earning a PhD in the social sciences, which basically gave me good coverage in inferential and applied statistics (T, F distributions, p-values, confidence intervals, linear regression, one-way and factorial ANOVA, PCA, etc.). I also took a machine learning graduate course at my university and a few online courses in introductory ML for R. All of this background gave me solid grounding in statistics. With all this I still find this book somewhat challenging, but definitely not too hard. I'd say without my background I would find this book hard to get through. There is linear algebra, concepts like minimizing cost functions, bias/variance tradeoff, learning from errors, etc. So, if you are just starting out or reading the previous sentence and don't know what I'm talking about, I would recommend learning more stats fundamentals before starting this.After you gain some proficiency in stats, come learn this book and elevate your understanding of the algorithms, add nuance to them, integrate them into your mental conceptual structures more fully (e.g. you'll know more nuances of ML, e.g. which subsets of algorithms are preferred for controlling more of the bias, variance, how random forest is basically bagging with a twist, how adaboost's treatment of classification errors has kind of an element of perceptron implementation, and many more).
(I own the 1st edition, and was given early access to a pre-release PDF of the 2nd ed. My paperback copy just arrived.)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 book will stay on your reference shelf for years to come!The authors clearly have taught these materials many times before, and their significant mathematical and technical prowess is delivered using a very approachable style. This book seems best suited for someone who wants to sit down and begin to apply Python Machine Learning to a problem that they already know they have. It's not particularly an "intro course to M.L.", but it contains enough details that you could easily follow along and learn how to use the various tools and techniques of the field if you've never seen or heard of them before.The copious notes scattered throughout this book are pure gold, mined from the obvious experiences of the authors while working in the field. If there ever is a Machine Learning equivalent to the venerable "Forrest M. Mims Engineering Notebook" for electronics, I feel these two authors could write it!Once you use this book to work on your current M.L. problem in Python, you will find yourself returning to it as a reference for other problems in the M.L. space. Its lucid explanations will help reinforce the topics presented, and cement your understanding of the materials.This book will get you writing Python Machine Learning code to work your current M.L. problem in no time flat!
I'm very pleased with this book. It includes complete practical approach to machine learning topic. Not only details about all algorithms, but also discusses various critical steps in preparing the data sets for ML work, evaluating the models using APIs from Scikit-Learn and Tensor Flow.Can't wait to attempt two RNN projects at the end of the book.
I found this book to be very clearly written and also very informative since in addition to providing code examples it tried to illustrate the basics of theory behind what makes machine learning work.The explanations were mainly done by showing examples of data on a x-y plot and how the different techniques separate the data to make a decision. This is a nice way to reduce the complexity of explanation and getting lost in the details of the mathematics and programming syntax etc and to get at the heart of where different algorithms have strengths.This is review is from the perspective of someone who knows a little python and had little knowledge of machine learning, but has kind of seen neural nets and regressions used in different applications over the years.Part of its usefulness to me is that it gives me a nice way to explain machine learning to non-scientists.
Great book on Python and Machine Learning. Raschka really knows his stuff. Having contributed to sklearn and writing the great mlxtend library he speaks with authority on the topics covered. I really learned a lot!
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