web site hit counter Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems - Ebooks PDF Online
Hot Best Seller

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

Availability: Ready to download

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 Pyth 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.


Compare

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 Pyth 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.

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

  1. 5 out of 5

    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.

  2. 4 out of 5

    Eryk Banatt

    I thought this book was a great overview of the actual practice of machine learning, and I think it compares favorably to something like Andrew Ng's "Machine Learning Yearning" which contains significantly less detail and no code. In general I think this was a pretty good beginning resource for using these frameworks, and it ends up feeling like reading structured documentation. I think this is a particularly useful part of this book, since in my experience a lot of this field is knowing which s I thought this book was a great overview of the actual practice of machine learning, and I think it compares favorably to something like Andrew Ng's "Machine Learning Yearning" which contains significantly less detail and no code. In general I think this was a pretty good beginning resource for using these frameworks, and it ends up feeling like reading structured documentation. I think this is a particularly useful part of this book, since in my experience a lot of this field is knowing which search terms to use - a task I think this book accomplishes rather neatly for its reader. I read through this book but I expect I will probably revisit it (along with the notes I took on it) when I need to actually do something it mentions. For example, I don't currently have any need for deploying a tensorflow model but I'm certain eventually it will be something I will need to know how to do. It's a nice reference point: I now vaguely know what to do for that, and know I have a good summary + examples available to me when I need it in the future. I did find that a number of the chapters were a little overly repetitive, but I think that's largely because I'm not exactly a beginner to keras, so I'm willing to not pay any mind to this. But for people who use some of these frameworks relatively often it can get a little boring to hear "if you want to make a custom version of , you can subclass the existing feature" for every feature. That said, in general I found that this book captured a nice balance between exposition and documentation. Especially something with as much wide functionality as scikit-learn, it can sometimes get to be a bit much to list out every technique in every module the way it might be in the actual sklearn documentation. However, Aurelien manages to pick out a few examples that capture the essence of the things you need to know, such that even if you've never seen something before you can easily figure out what's happening (i.e. intoducing sklearn.manifold.TSNE after not mentioning it, but mentioning sklearn.manifold.locally_linear_embedding; you might not really know what TSNE is if you've never heard it before (if you live under a rock), but you can probably easily infer from context what it's supposed to be doing). You don't need to know every single classifier available in something like sklearn, all you really need to know is that they're all mostly drop-in replacements for each other, and you can look up the details later in the actual documentation if you forget. Likewise, tensorflow 2 really just seems like keras 2, so I'm pretty excited about playing around with it a bit more. My opinions on the actual framework are probably not super within the scope of this book, though, so I won't bother detailing them here. In general, a pretty good intro for beginners, and fairly easy to get through if you just want to know how to use a framework you previously didn't know how to use (like tf 2.0)

  3. 4 out of 5

    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.

  4. 4 out of 5

    Kamil

    What a read! It's truly one of the best possible looks into the world of the machine learning. It helped me a lot to focus on the most crucial aspects for building usable ML models. What a read! It's truly one of the best possible looks into the world of the machine learning. It helped me a lot to focus on the most crucial aspects for building usable ML models.

  5. 5 out of 5

    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!

  6. 5 out of 5

    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.

  7. 4 out of 5

    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.

  8. 4 out of 5

    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!

  9. 4 out of 5

    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.

  10. 4 out of 5

    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.

  11. 4 out of 5

    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.

  12. 5 out of 5

    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.

  13. 5 out of 5

    Derek Bridge

    In effect, the second edition of this volume, expanded to 800 pages, now including Keras, TensorFlow 2, unsupervised learning, updated neural network architectures, and a whole lot more. The explanations are mostly clear, with the use of Keras making a huge improvement over the previous volume's focus on TensorFlow. It's a hugely impressive piece of work. Recommended! In effect, the second edition of this volume, expanded to 800 pages, now including Keras, TensorFlow 2, unsupervised learning, updated neural network architectures, and a whole lot more. The explanations are mostly clear, with the use of Keras making a huge improvement over the previous volume's focus on TensorFlow. It's a hugely impressive piece of work. Recommended!

  14. 5 out of 5

    Lars

    Very comprehensive This 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).

  15. 4 out of 5

    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.

  16. 5 out of 5

    Alan Couzens

    Without question, the best book on ML that I have read. Covers all subsets - supervised learning, unsupervised learning, RL & practical application of each both locally and using cloud services. Explores the background of each algorithm in sufficient depth but also takes a very practical approach. The book that I frequently return to as a "how to" reference. Without question, the best book on ML that I have read. Covers all subsets - supervised learning, unsupervised learning, RL & practical application of each both locally and using cloud services. Explores the background of each algorithm in sufficient depth but also takes a very practical approach. The book that I frequently return to as a "how to" reference.

  17. 5 out of 5

    Suphan Fayong

    I believe this is the most comprehensive Machine Learning (ML) book covering all fundamental aspects of ML plus many advanced topics. The book is well written in most parts. As of today (April 2021), the content is still up to date and contains many modern concepts. This book is well suited for someone wanting to explore ML thoroughly.

  18. 5 out of 5

    John Doe

    Seriously hands on. Basically step by step code presentation, just like reading official documentation but with explaining words. However, TensorFlow 2 already came out, parts of the code in the book are outdated even for TensorFlow 1.x. Be careful with the code, and remember to check official docs.

  19. 5 out of 5

    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.

  20. 5 out of 5

    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.

  21. 4 out of 5

    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.

  22. 5 out of 5

    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.

  23. 4 out of 5

    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.

  24. 5 out of 5

    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.

  25. 4 out of 5

    LeoQuiroa

    This is the longest book I have ever read. I spent 4 months understanding the concepts, doing the exercises, reviewing concepts, and learning a lot. Using the Dunning-Krugger effect graph, I feel in the valley of the knowledge, however, that is good because the confidence/expertise will start to grow from now on. I absolutely recommend this book. You will feel Part I really easy and even bored. But, Part II, will start to challenge you a lot in every chapter.

  26. 4 out of 5

    Aftab

    Great overview of the various aspects of setting up a machine learning pipeline. The different algorithms involved For supervised, unsupervised and ensemble training. Including how to implement those in a practical way.

  27. 4 out of 5

    Randy Hines

    This is an absolutely essential read in exploring the topic. The code snippets and recipes are great, and the color illustrations in the print version make this a very enjoyable text. I often refer back to particular sections for methods when tackling real problems.

  28. 5 out of 5

    Alen

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

  29. 4 out of 5

    Mehdi

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

  30. 5 out of 5

    Mehdi Zare

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

Add a review

Your email address will not be published. Required fields are marked *

Loading...