Artificial Intelligence

Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems

Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems

  • Downloads：5718
  • Type：Epub+TxT+PDF+Mobi
  • Create Date：2021-03-05 03:12:20
  • Update Date：2025-09-07
  • Status：finish
  • Author：Aurélien Géron
  • ISBN：1492032646
  • Environment：PC/Android/iPhone/iPad/Kindle
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Summary

Through a series of recent breakthroughs， deep learning has boosted the entire field of machine learning。 Now， even programmers who know close to nothing about this technology can use simple， efficient tools to implement programs capable of learning from data。

The updated edition of this best-selling book uses concrete examples， minimal theory， and two production-ready Python frameworks--Scikit-Learn and TensorFlow 2--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems。 Practitioners will learn a range of techniques that they can quickly put to use on the job。 Part 1 employs Scikit-Learn to introduce fundamental machine learning tasks， such as simple linear regression。 Part 2， which has been significantly updated， employs Keras and TensorFlow 2 to guide the reader through more advanced machine learning methods using deep neural networks。 With exercises in each chapter to help you apply what you've learned， all you need is programming experience to get started。

NEW FOR THE SECOND EDITION: Updated all code to TensorFlow 2Introduced the high-level Keras APINew and expanded coverage including TensorFlow's Data API， Eager Execution， Estimators API， deploying on Google Cloud ML， handling time series， embeddings and more

With Early Release ebooks， you get books in their earliest form--the author's raw and unedited content as he or she writes--so you can take advantage of these technologies long before the official release of these titles。 You'll also receive updates when significant changes are made， new chapters are available， and the final ebook bundle is released。

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Reviews

Chen Chen ，

This is hands-down the best ML/deep learning/AI book that I ever read。 A must-have for beginners or students who learned PyTorch in class but want to learn the other side in a practical way。

Amanda ，

One of the best machine learning Python resources out there。 Highly recommended。

Piush Kumar ，

Superb book on ML。 It covers complete machine learning from regression using Scikit。 Although make sure you have certain understanding of python and college level mathematics before reading this book。 I highly recommend this book。

Minervas Owl ，

Great book on deep learning and other machine learning methods。 Clearly written and provides many intuitive explanations， examples， and codes。 The Deep learning part covers some latest papers such as Attention is All You Need （2017） and StyleGan （2018）。 I find the logic behind the tensorflow data pipeline grammar （chap 16） hard to grap and wish the author could explain more， but it could be just me。

Nadiantara Wayan ，

If you already have basic knowledge of calculus， linear algebra， statistics， and programming with Python， this book is just the best for machine learning applications。

Islomjon ，

I was looking for such book a long time since I started to learn Machine Learning。 It is very broad and useful in its scope。 Book examines traditional machine learning algorithms as well as Artificial Neural Networks。 Some summary to popular algorithms with flawless visualization techniques。

Praful Mohanan ，

A superb go to supplement book with the theoretical lectures from Andrew Ng。 If you are already comfortable with the theory， this book is handy for doing the practical hands-on approach。 It first focuses on Machine Learning using scikit learn right from framing a problem then focuses on Deep Learning with Keras and Tensorflow。 Highly recommended book。

Albert ，

I can't say that this is for beginners。 I've come up to regression chapter and finished it， I copied the code and make it run but I can't say that I understand all the information provided。 I'm a programmer and still didn't get lots of ideas。And when I tried the Coursera online course， and finished the first course out of 4。 I've understand it very well。 It explains little by little the concept of Machine Learning。And after that I come back to this book and things makes sense now。So for those be I can't say that this is for beginners。 I've come up to regression chapter and finished it， I copied the code and make it run but I can't say that I understand all the information provided。 I'm a programmer and still didn't get lots of ideas。And when I tried the Coursera online course， and finished the first course out of 4。 I've understand it very well。 It explains little by little the concept of Machine Learning。And after that I come back to this book and things makes sense now。So for those beginner like me， you can try the online courses first then try this book and I'm pretty sure that you will understand the content in this book better。 。。。more

Andy ，

We are reading this book for the book club at Synopsys。 We are pretty far in and I am scheduled to present Chapter 7 next week。This is a very practical overview of machine learning techniques using Python。 The first couple books I read on it included stuff like "how to design machine learning algorithms"。 This book is more about showing the huge volume of algorithms that are already developed and easy to access。 Pretty much every optimization and tweak you might think of is there， you just need We are reading this book for the book club at Synopsys。 We are pretty far in and I am scheduled to present Chapter 7 next week。This is a very practical overview of machine learning techniques using Python。 The first couple books I read on it included stuff like "how to design machine learning algorithms"。 This book is more about showing the huge volume of algorithms that are already developed and easy to access。 Pretty much every optimization and tweak you might think of is there， you just need to know how to find it。 The scope is somewhat overwhelming， but basically we are learning what there is out there and how to pick the technique based on your dataset。We are doing a chapter every 2 weeks， and that requires quite a bit of time to do it right。 For example， just reading the chapter is 1-2 hours。 You probably want to enter the code into Jupyter or some other engine to follow along。 Getting all the side packages installed takes time （Jupyter， graphviz， Kaggle data sets）。 Then you can spend hours on the exercises， or dive off into something of your own， both of which take as long as you care to spend。Will I continue the book after I retire next month？ We'll see！ 。。。more

Diego Maye ，

Because I started Master Degree in Data Science I made some research for a good book， Hands-On ML was one of books in list but I start with Deep Learning with Python since main language in ML courses in Master was Python， and read a couple more but really no finish left on beginning to be sincere I really enjoy Hands-On over the other books I read and really recommend it to those who want to start in ML world and understand how it works。

Hiran Hasanka ，

I was in for a treat！！ Completely blown away from the beginning。 I think this is one of the most interesting text books I've ever read。 Can be recommended for all types of machine learning learners since it has lessons that start from ML 101 to more complex and sophisticated topics at the end。 I was in for a treat！！ Completely blown away from the beginning。 I think this is one of the most interesting text books I've ever read。 Can be recommended for all types of machine learning learners since it has lessons that start from ML 101 to more complex and sophisticated topics at the end。 。。。more

Özgür ，

One of the best for ML。。。

Minh Son Nguyen ，

In my opinion， this is still the best technical book about ML and AI， I've read so far。 The balance between theory and practice suits me very well。Nevertheless， this is not for beginner。 You must be confident to read and code in Python also with main scientific libraries like numpy， matplotlib。 More importantly， good mathematics knowledge is important， so it's better to review linear algebra， calculus， probability theory and statistics beforehand。The book consists of two parts:1。 Machine Learnin In my opinion， this is still the best technical book about ML and AI， I've read so far。 The balance between theory and practice suits me very well。Nevertheless， this is not for beginner。 You must be confident to read and code in Python also with main scientific libraries like numpy， matplotlib。 More importantly， good mathematics knowledge is important， so it's better to review linear algebra， calculus， probability theory and statistics beforehand。The book consists of two parts:1。 Machine Learning: All basic and common concepts。 Exercises are mostly about using sklearn lib。 I think， author cover most topics well enough and exercises are good。 Although some of them are not my main interests。2。 Deep Learning: This is quite heavy。 Sometimes I found that the author didn't explain the theory good enough。 I need to refer to "Deep Learning" by Ian Goodfellow to have better idea of the mathematics behind CNN and RNN。 Also in this part， Computer Vision， Generative Learning and Reinforcement Learning are not the interesting topics for me at this point so I read through them quickly without doing coding exercises。 Maybe I will come back to those in the future。Appendices are quite useful actually， so I also recommend to read through all of them。 。。。more

Anthony ，

The book gives a pretty good overview of how to use Scikit-Learn and Keras/TensorFlow。 There is math providing the details behind some of the black boxes。 Also， you don't need to be much of an expert at Python to be able to follow the code and use the examples。 One annoying thing about the book: the author loves the word "simply"。 To do anything， "you simply do this" or "you simply do that。" It gets rather tedious after you read that word over and over。 I think， for the next edition， the author The book gives a pretty good overview of how to use Scikit-Learn and Keras/TensorFlow。 There is math providing the details behind some of the black boxes。 Also， you don't need to be much of an expert at Python to be able to follow the code and use the examples。 One annoying thing about the book: the author loves the word "simply"。 To do anything， "you simply do this" or "you simply do that。" It gets rather tedious after you read that word over and over。 I think， for the next edition， the author should get a better editor， or do a global search/replace for "simply" in all its forms。 。。。more

Vladimir Georgiev ，

Great for beginners in machine learning with a lot of examples。 If you are scared of math， it is reduced with just a few equations and a lot of explainantions。

Minh Long ，

This is the first time I review a 。。。 kind of like a textbook。When I was in my second year in University， I decided to learn Machine Learning， and every page suggested this book。 Nevertheless I bought it， and it turns out the book is super helpful。 You get your hands on real projects， data， you build real applications， you use the model to run on your own dataset。 The book covers most of the fields in ML， from traditional supervised and unsupervised learning to deep learning like Neural network， This is the first time I review a 。。。 kind of like a textbook。When I was in my second year in University， I decided to learn Machine Learning， and every page suggested this book。 Nevertheless I bought it， and it turns out the book is super helpful。 You get your hands on real projects， data， you build real applications， you use the model to run on your own dataset。 The book covers most of the fields in ML， from traditional supervised and unsupervised learning to deep learning like Neural network， computer vision， and time-series predictions， even Reinforcement learning。 It is not totally a top-down approach， since the author did include mathematics behind it， still it is not as clear as the book "Deep Learning" by Goodfellow et al。， but still helps me to understand the intuition behind it。Totally recommend for new learners！ 。。。more

Giacomo Rebonato ，

This is a long good book to keep for reference。But I think that exist more easy approaches on the subject。

Andrés Hernández ，

This is an absolute banger of a book。 Excellent balance between putting the algorithms on practice， what’s happening under the hood， and important parameters。 It is a must read if you’re getting into ML。

Timoteo ，

Not for beginners， but absolutely complete

Yanwei Liu ，

An awesome ML book for beginners to have more hands-on experience in Machine Learning and Deep Learning。There're some tricks and best practices inside this book that can be the secret weapon to your model。I highly recommend this book for programmers who want to taste the favor of ML and DL。 An awesome ML book for beginners to have more hands-on experience in Machine Learning and Deep Learning。There're some tricks and best practices inside this book that can be the secret weapon to your model。I highly recommend this book for programmers who want to taste the favor of ML and DL。 。。。more

Mehdi Zare ，

A great collection of all you need to start using more advanced machine learning packages

Lorenzo Reyes ，

This is not a textbook， it's a code repository。This book doesn't explain how the models work just teaches you how to code the model， it just gives you some equations and tries to explain them in a few lines。 This is not a textbook， it's a code repository。This book doesn't explain how the models work just teaches you how to code the model， it just gives you some equations and tries to explain them in a few lines。 。。。more

Deniz ，

I want to rate this almost 4。5 stars :）Disclaimer: I have read the Scikit-Learn portion in full， and the Keras and Tensorflow portion through Convolutional Neural Networks， which I am using in an imaging project。The author crams a lot of material in a short space and expects you to pick up *all* that he's putting down。 Even though this takes a more hands-on approach as compared to theoretical juggernauts， the author presents enough theory to ground the practical aspects。A lot of the tips and tri I want to rate this almost 4。5 stars :）Disclaimer: I have read the Scikit-Learn portion in full， and the Keras and Tensorflow portion through Convolutional Neural Networks， which I am using in an imaging project。The author crams a lot of material in a short space and expects you to pick up *all* that he's putting down。 Even though this takes a more hands-on approach as compared to theoretical juggernauts， the author presents enough theory to ground the practical aspects。A lot of the tips and tricks would not have occurred to me。I find the exercises at the end of every chapter and the provided solutions very useful。 In addition， there is the extended version of the notebooks within every chapter at https://github。com/ageron/handson-ml2 say if you want to recreate the visualizations in notebooks of your own。 。。。more

Douglas ，

The best book about the subject out there。 It contains easy to understand code in Python and covers from simple linear regression to RNN and CNNs that were published a few months before the launching of the book。 A must have。

Mehdi ，

Amazing book！ Great explanations and nice visualization。 I will probably keep rereading it as needed

Ahmad Hamdi Emara ，

Even if you're not interested in actually specializing in data science or machine learning， this book is a treasure for every developer to acquire and read at least once。 Even if you're not interested in actually specializing in data science or machine learning， this book is a treasure for every developer to acquire and read at least once。 。。。more

Matthew Perez ，

I spent the last five months learning the math and theory behind machine learning， but when I finally tried to do something on a simple Kaggle set， I was drawing blanks。 This book really showed me what I was missing: context。 It doesn't just demonstrate different tools， it gives you a framework that you can apply to any problem （chapter 2） and how to think about what you're doing in each phase of an ML project。 It doesn't baby you on the math， but it doesn't go deeper than it needs to either。 I I spent the last five months learning the math and theory behind machine learning， but when I finally tried to do something on a simple Kaggle set， I was drawing blanks。 This book really showed me what I was missing: context。 It doesn't just demonstrate different tools， it gives you a framework that you can apply to any problem （chapter 2） and how to think about what you're doing in each phase of an ML project。 It doesn't baby you on the math， but it doesn't go deeper than it needs to either。 I think the same can be said for the coding。 This book is all about connecting and implementing the basics in a solid manner。 For me， that's exactly what I'm looking for。It really has been the missing link for me on my self-study to connect theory to application and I'm really happy to have picked it up。 If you feel like you're in a similar position， I highly recommend that you pick up a copy。 I also recommend doing the coding exercises as you read them。 It'll reinforce what you're learning and also keep you from outpacing yourself。 Take the time to enjoy the awesome journey that is Hands-On ML。 。。。more

Lars ，

Very comprehensiveThis book finally got me started on machine learning - something I have not managed with a lot of other resources。 It covers a lot of ground， both in theory and the practical application。 Recommended。 Just make sure that you do not buy the Kindle version， as others pointed out （I bought the hardcover）。

Alen ，

The best book about ML。 Great readability and writing style with many interesting touches。

Dan ，

This book was a fantastic surface-level introduction to a vast array of machine learning methods， including their implementation in Scikit-Learn， Keras and Tensorflow （2。0）。 It's written in a casual style， which makes the flow a lot better compared to terse textbooks。 The newest version also covers new concepts such as the Transformer architecture for natural language processing， as well as Generative Adversarial Networks and reinforcement learning。Sometimes the author gets a bit bogged down on This book was a fantastic surface-level introduction to a vast array of machine learning methods， including their implementation in Scikit-Learn， Keras and Tensorflow （2。0）。 It's written in a casual style， which makes the flow a lot better compared to terse textbooks。 The newest version also covers new concepts such as the Transformer architecture for natural language processing， as well as Generative Adversarial Networks and reinforcement learning。Sometimes the author gets a bit bogged down on implementation and spends way too long on the technical details such as exact specifications of the APIs of the different libraries; essentially things that you could just look up anyway and which doesn't add any knowledge。 A small "quick start" and a reference associated to every theoretical concept would've been more beneficial， and also made the book a couple of hundred pages shorter I think。Overall a solid recommendation however。 It'll probably be my go-to reference for overall ideas of the various concepts， whenever I need a refresher。 。。。more

Reference

google book https://www.google.com/search?tbm=bks&q=Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems

hathitrust digital library https://babel.hathitrust.org/cgi/ls?q1=Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems&field1=ocr&a=srchls&ft=ft&lmt=ft

The British Library https://bll01.primo.exlibrisgroup.com/discovery/search?query=any,contains,Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems&tab=LibraryCatalog&search_scope=Not_BL_Suppress&vid=44BL_INST:BLL01&lang=en&offset=0