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1. Critical
   This book is critical for my PhD research and was necessary preparation for my prelim exam. This is an in-depth treatment of ML and it provides many reference publications inline, which are valuable for further research when you’re about to publish papers related to the specific topics.

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2. Nice book
   This is a good book for every level of programmers and ml enthusiasts

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3. Excellent Textbook for Hands-On Learning of ML
   This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book.For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch.For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min).From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures.I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications.Several textbook editions later, what is different about this new edition?First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills.Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like.Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.

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4. Good Content. Bad Presentation
   The book covers a wide range of useful terms in the never-ending machine learning landscape. The pages are on black and white style and the relevance of explained concepts are far from perfect.

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5. One of the best machine learning books…
   Machine Learning can often be intimidating whether you are starting out or already a practitioner. It is easy to get stuck on one concept, walk away frustrated, or just copy that code you find on StackOverflow without really understanding what it does. What the authors of this book, Machine Learning with PyTorch and Scikit-Learn, have managed to do is to keep the reader engaged giving a deeper illustration as to how the concepts work. In this book, you get practical code examples, a detailed explanation of how the various library tools work, and exposure to the mathematical concepts behind machine learning algorithms. In addition, what I like about the book unlike many machine learning books is that the authors have managed to intuitively explain how each algorithm works, how to use them, and the mistake you need to avoid.I have not read a Machine Learning book that better explains Transformers as this one does. The authors have managed to give a detailed dive into this model architecture through well-explained codes and illustrations. As a reader, you walk away having intuitively grasped the concepts of attention and self-attention in ways that will make this crucial NLP architecture clear. You get exposed to pre-trained models from HuggingFace library which really helps to have that hands-on experience working with large datasets.As they have done throughout the book, the authors have broken down those complex mathematical operations into simple explanations that are easy to follow. What I generally like about the book is how it seamlessly connects all the chapters, not throwing off the reader. There are numerous external resources quoted throughout the book. This helps spark that curiosity to dig deeper. In addition, you get introduced to PyTorch, getting exposed to all those sophisticated libraries that help the reader learn how to maximize their compute power. I would say it is not intimidating at all even if you have not used PyTorch before.I would recommend this book to anybody seeking a textbook that is both easy to read and modern in its content. If were to rate the book I will give it a 10/10 as it really applies to both beginners and experienced practitioners, covers all the concepts one needs to apply in their operations, and acts as a quick reference.

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6. A great AIML Book and covers a lot of topics.
   A great book and covers a lot of topics. It does not go as deep on Pytorch as I had expected but it does cover a lot of ML topics. This does cover the Pytorch a lot and provides a good balance in contents – depth in one topic vs coverage of different topics.Note that the contents are in black and white but a color version is available for free download once you buy the book.I found the GitHub source codes helpful.

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7. Great to catch up after several years away
   This is a fantastic book. It’s huge, but the pages go quickly. I’m my case, I wanted to catch up from a few years away from ML and learn PyTorch at the same time. Really enjoyed the book.

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8. Excelente libro

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9. Usually I ordered lots of the book , but it’s the best quality book .I saw in review about the page quality, but it’s one of the best book I purchase.Also content of book is good , must recommended if you are planing to learn ML

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10. La materia è immensamente vasta e ce ne vorrebbero 10 almeno libri come questo, per spiegarla e conoscerla discretamente. Tuttavia questo libro fornisce delle basi sufficienti ed esaustive per padroneggiare l’argomento e poter iniziare a smanettarci sopra in modo piuttosto funzionale e scalabile. Implica delle basi solide della programmazione e del linguaggio Python, nonchè di algebra lineare, statistica e calcolo differenziale, altrimenti è meglio evitarlo poichè non ci si capirebbe nulla, nonostante gli argomenti siano generalmente spiegati al meglio. Validissimo per utenti di calibro medio-avanzato.

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11. This book provides a perfect Balance between theory and practise as well covering a wide range of possible applications. One point of criticism would be the physical quality of the book (the Page were wavy and the print is in Black and white), however since there is a free eBook download that comes with the product thats not really an issue. Would recommend to anyone looking for a Sound introduction into machine learning

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12. I have skimmed through the book and it seems to be exactly what I’m looking for in order to deepen my ML skills. Though, unfortunately, the book material is bad. First, they have cut it badly, so the page numbers aren’t visible. Second the paper and print quality is not the great and “looks” cheap. I expected more for a 50$ book to be honest. As they included the free PDF, I don’t want to hazzle with returning the book as I want to start learning right away. 5/5 for the content, 1/5 for the quality of the print

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