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

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 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 mathematics and programming syntax etc and to get at the heart of where different algorithms have strengthsThis 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 yearsPart of its usefulness to me is that it gives me a nice way to explain machine learning to non scientists El Gaucho Martín FierroLa vuelta de Martín Fierro 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 Fragonard Art and Eroticism 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 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 Mao Zedong 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 mathematics and programming syntax etc and to get at the The Monarchs Are Missing heart of where different algorithms Touchstone have strengthsThis is review is from the perspective of someone who knows a little python and Chocolate Candy Always Melts In The Sun Poems AboutLove betrayal anger struggle and understanding had little knowledge of machine learning but Love is Blind has kind of seen neural nets and regressions used in different applications over the yearsPart of its usefulness to me is that it gives me a nice way to explain machine learning to non scientists

### REVIEW ↠ JIMFORD.CO.UK Ò 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 Reiki A Comprehensive Guide 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 জামশেদ মুস্তফির হাড় hidden patterns and structures in data with clusteringDelve deeper into textual and social media data using sentiment analysi I am impressed about Memoirs of Madame de La Tour du Pin 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

### Sebastian Raschka Ò 1 FREE DOWNLOAD

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 Under Her Command (The Bosss Pet, 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 El Gaucho Martín FierroLa vuelta de Martín Fierro 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 Fragonard Art and Eroticism 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 Mao Zedong 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 The Monarchs Are Missing hard I d say without my background I would find this book Touchstone 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 Chocolate Candy Always Melts In The Sun Poems AboutLove betrayal anger struggle and understanding how random forest is basically bagging with a twist Love is Blind how adaboost s treatment of classification errors Straight To Sleep Gay Somnophilia 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 sku

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 s

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 Learni

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 a

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 sepa

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

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 sci

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 the why behind key concepts of machine learning