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Sebastian Raschka, Vahid Mirjalili [ Read ] Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow – Kindle eBook, TXT and Epub Download



10 thoughts on “Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

  1. says:

    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 b

  2. says:

    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

  3. says:

    (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, Machin

  4. says:

    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 optimi

  5. says:

    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 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 online.

    I'm giving this book one star because the writers are lazy they

  6. says:

    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

  7. says:

    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 administrators turned "data scientists" who don't understand statistics or data but want to get some results that proba

  8. says:

    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 backgr

  9. says:

    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.

  10. says:

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

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Read ´ E-book, or Kindle E-pub Ê Sebastian Raschka, Vahid Mirjalili

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. 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 itPS The book focus on supervised and unsupervised machine learning methods but not much about reinforcement learning The Line 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 Your Naughty Playmate 3 - Cuckolding Fantasy patterns and structures in data with clusteringDelve deeper into textual and social media data using sentiment analysi. 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 Haute Chinese Cuisine from the Kitchen of Wakiya practice those theories I will recommend itPS The book focus on supervised and unsupervised machine learning methods but not much about reinforcement learning

Summary Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

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. This book will stay on your reference shelf for years to comeThe 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 ML but it contains enough details that you could easily follow along and learn how to use the various tools and techniues of the field if you ve never seen or heard of them beforeThe 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 euivalent to the venerable Forrest M Mims Engineering Notebook for electronics I feel these two authors could write itOnce you use this book to work on your current ML problem in Python you will find yourself returning to it as a reference for other problems in the ML space Its lucid explanations will help reinforce the topics presented and cement your understanding of the materialsThis book will get you writing Python Machine Learning code to work your current ML problem in no time flat Entrepreneurial Vernacular practical approach to key frameworks in data science machine learning and deep learningUse the most Advanced C Programming by Example powerful Python libraries to implement machine learning and deep learningGet to know the best Poslije svega (After, 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. This book will stay on your reference shelf for years to comeThe authors clearly have taught these materials many times before and their significant mathematical and technical Die Herrenschneiderei 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 Daisy Malone and the Blue Glowing Stone problem in Python you will find yourself returning to it as a reference for other Pretend God Is Deaf problems in the ML space Its lucid explanations will help reinforce the topics Cased Images & Tintypes KwikGuide presented and cement your understanding of the materialsThis book will get you writing Python Machine Learning code to work your current ML Las Puertas Del Amor problem in no time flat

Read ´ E-book, or Kindle E-pub Ê 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