# [read Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow] PDF author Sebastian Raschka, Vahid Mirjalili

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- Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow
- Sebastian Raschka, Vahid Mirjalili
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- 10 September 2019 Sebastian Raschka, Vahid Mirjalili
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## 10 thoughts on “Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow”

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### Sebastian Raschka, Vahid Mirjalili â 8 Read

S 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 1x deeply than ever before and get essential coverage of the Keras neural network library along with updates to scikit learn 0181What you will learnUnderstand the key frameworks in data science machine learning and deep learningHarness the power of the latest Python open source libraries in machine learningExplore machine learning techniues using challenging real world dataMaster deep neural network implementation using the TensorFlow 1x libraryLearn the mechanics of classification algorithms to implement the best tool for the jobPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringDelve deeper into textual and social media data using sentiment analysi. Easy to read well structured and very useful The only caveat I would add is that this is for Python programmers who have a reasonable background in maths but are new to ML not those in ML looking to pick up Python

### Read & Download ✓ eBook, ePUB or Kindle PDF â Sebastian Raschka, Vahid Mirjalili

K Python Machine Learning Using Python's open source libraries this book offers the practical knowledge and techniues you need to create and contribute to machine learning deep learning and modern data analysisFully extended and modernized Python Machine Learning Second Edition now includes the popular TensorFlow 1x deep learning library The scikit learn code has also been fully updated to v0181 to include improvements and additions to this versatile machine learning librarySebastian Raschka and Vahid Mirjalili's uniue 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 opportunitiesIf you've read the first edition of this book you'll be delighted to find a balance of classical idea. If you didn t buy the first edition and are looking to dive into machine learning with python then I would highly recommend this bookThe only change to this book was the inclusion of Tensorflow and the removal of Theano The examples they use are the same that everyone uses MNIST IMDB Cat vs Dogs you can find these same parroted tutorials anywhere onlineI m giving this book one star because the writers are lazy they ultimately just repackaged their previous edition into a new book

### Free read Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

Publisher's Note This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries A new third edition updated for 2020 and featuring TensorFlow 2 and the latest in scikit learn reinforcement learning and GANs has now been publishedKey FeaturesSecond edition of the bestselling book on Machine LearningA practical approach to key frameworks in data science machine learning and deep learningUse the most powerful Python libraries to implement machine learning and deep learningGet to know the best practices to improve and optimize your machine learning systems and algorithmsBook DescriptionMachine 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 boo. I own the 1st edition and was given early access to a pre release PDF of the 2nd ed My paperback copy just arrivedThis 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 doingWhat 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 euations 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 build

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 learn

I would say it's not a beginner's book, but f

This book will stay on your reference shelf for years to come!

The authors clearly have taught these materials many times before, and

(I own the 1st edition, and was given early access to a pre release PDF of the 2nd ed. My paperback copy just arrived.)

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Very steep learning curve.

I almost gave up in chapter two at perceptron but since that algorithm is the foundation of all I spent a whole week to understand it. The code the author uses is pretty much optimized and it was not in sync with the mathematical introduction. But the first 30 pages are absolutely neccessary to read a

If you didn't buy the first edition, and are looking to dive into machine learning with python, then I would highly recommend this book.

The only change to this book was the inclusion of Tensorflow and th

I purchased two Packt publications on AI and ML. Both are extremely poorly written, poorly researched and extremely difficult to follow. Language, terms, descriptions and content are difficult to follow at best, or archaic at worst. Nothing is explained and require additional research at almost every step. Screenshots sizes are inconsistent, do not add value and in many cases are blown up to an extent where screenshot fo

Basic multivariate statistics methods wrapped up in fancy machine learning terminology, which all comes down to methods that were around for decades to say the least. This is one of the books for the SQL data base administra

Easy to read, well structured and very useful. The only caveat I would add is that this is for Python programmers

I am impressed about how this book was designed, its layout is very logic and can take you from the basic terms to complicated knowledge, action is louder than speaking, it also use Scikit learn to teach newbies like me to practice those theories, I will recommend it.

P.S. The book focus on supervised and unsupervised machine learning methods, but not much about reinforcement learning.

I’m using this book alongside the machine learning nanodegree by Udacity and it’s brilliant in explaining the why behind key concepts of machine learning!