# Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow (Pdf) ✓ Sebastian Raschka – Book, TXT or Kindle free

- Kindle
- 622pages
- Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow
- Sebastian Raschka
- English
- 05 September 2018 Sebastian Raschka
- 9781787125933

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

### Leave a Reply

### Sebastian Raschka ☆ 6 SUMMARY

On 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 x deep learning library The scikit learn code has also been fully updated to v 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 ideas 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 reuire 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 fonts well exceeds the book print This gives the impression images are added to maximize uptake of the book print real estate In some cases you have to work out that some instructions are included in screenshots rather than within the actual text I have come to the conclusion that Packt publications are no than r sum fillers for their authors The publication includes no references I learned from YouTube videos Beware of catchy one liners such as A Comprehensive Guide to Building Intelligent Apps for Python Beginners and Developers This is nothing than bait The book is an insult to the word comprehensive and the best use python beginners may make of this publication is to send it back immediately for a refund and look for an academic and referenced text Item not as described

### REVIEW â PDF, eBook or Kindle ePUB ☆ Sebastian Raschka

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 xdeeply than ever before and get essential coverage of the Keras neural network library along with updates to scikit learn What 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 x 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

### READ & DOWNLOAD Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

Publisher's Note This edition from is outdated and is not compatible with TensorFlow or any of the most recent updates to Python libraries A new third edition updated for and featuring TensorFlow 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 book Pyth This book is excellent for the following demographicPeople who already have a decent level of skill and experience in statistics who want to 1 Elevate their understanding of ML techniues 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 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 biasvariance 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 stats fundamentals before starting thisAfter 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 fully eg you ll know nuances of ML eg which subsets of algorithms are preferred for controlling 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

This book is excellent for the following demographicPeople who already have a decent level of skill and experience in statistics who want to 1 Elevate their understanding of ML techniues without absolutely breaking their skull on dense theory 2 Learn how to implement the algorithms in Python and gain moderate

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

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

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 th

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 workThe explanations were mainly done by showing examples of data on a x y plot and how the different techniues 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 ma

I purchased two Packt publications on AI and ML Both are extremely poorly written poorly researched and extremely difficult

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 SL data base administrators turned data scientists who don't understand statistics or data but want to get some results that probably don't mean a

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

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

I’m using this book alongside the machine learning nanodegree by Udacity and it’s brilliant in explaining t