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This book gives you a hands-on approach to learning by doing. As opposed to the trendy deep learning books that dive deep into the weeds from the start, this book starts with the more traditional ML approaches (the Scikit-learn part) giving you a great deal of context and practical tools for solving all kinds of problems. Only after does he transition into deep learning concepts, giving you both a great overview and the background to understand when and where to apply the various techniques. Its code-focused so you’ll have the option to run working code on real problems throughout the book.Most important for me, he focuses on explanation over hand-wavy equations that are rampant in other ML books. I say hand-wavy because they typically go like so: “Here’s a hard concept. Rather than explain it well, I’ll give you some linear algebra and calculus equations, remind you that this is stuff you should have learned in high school, and then move on.” Authors probably feel justified in doing this, but after reading a book like this you understand what they are really doing: Skipping the hard-part of breaking difficult concepts down into chunks that can be consumed by a competent programmer, who is perhaps not an expert in “high school” math. Moreover, this author does so without dumbing down the content. That’s the mark of someone who well understands both the content and the audience.This book is long and dense, and serves as both a guide and a reference. It is not a quick read / overview or light reading type book.
I’ve read all of the predominant machine learning related python books and this one is by far the best one. I was excited to see the second edition of this book come out. It is packed with new information (1.5x the length of the first edition) and updated for TensorFlow 2. I have the Kindle edition and find it very helpful to highlight key points. I look forward to receiving the print edition as well once it is released.EDIT: Just received the print edition of the book and it’s in color! The first edition wasn’t. This is a pleasant surprise as it makes it easier to read with various charts and graphics.
While I enjoy learning from this book, the math font in kindle edition is a mess which makes the reading unpleasant. I know to some this probably shouldn’t be a deal breaker but for someone who wants to move from hard copy to kindle, it was a disappointment.
This is an update for my previous review.Recently, I gave one star for the poor ebook experience but with author’s comment I realized the publisher updated the ebook and now everything is great in the ebook.As the name suggests, the book gives you a really hands-on experience on machine learning. This covers most of the recent main advancements in the field.
I’m very pleased with this book. I enjoy the little bits of humor here and there, and it does a great job not glossing over important details that might be a stumbling block for someone. I’m quite comfortable with python however I appreciated that he did go into depth on setting up virtual environments and best practices. I remember years back when I was starting that whole concept tripped me up so much, having this explained so well is going to save someone a lot of time. Also his code seems so far to be written in a very thoughtful way and has them all on github. He also goes into lots of gotchas and tips and tricks that just overall seem to add a certain maturity to his writing. He has obviously very well versed in machine learning.Overall I would recommend. It’s been much more interesting than I expected.
Aurelien did it again!Whether you are a data scientist looking to start building predictive models in Python, or a software developer looking to become an ML engineer, look no further!The excellent balance between theory/background and implementation that was present in the first edition is kept, with the essential material additions made (e.g. the unsupervised learning in the “classical ML” part, or the Keras API, which is quickly becoming the most popular way to use TensorFlow).Needless to say, the Jupyter notes accompanying each chapter are more than helpful.Also, as a cherry on top, the illustrations in the printed version are now in color, which makes it even easier to read.In summary, this book is an absolute must-have for a Python-rooted data scientist / ML engineer!
I’m finishing up an MCS in Data Science from UIUC and I can tell you bar none that this book should be required reading in this subject. The required ML course at school was so confusing and they assumed WAY too much. Reading over the same topics in this book was like night and day in terms of explaining things in a way that makes sense. The images, graphs, and tables are clear and help a lot by providing visuals to the text explanation. I did notice a few typos but so far nothing critical. This is not a light read as it comes in at almost 800 pages but taking it model-by-model is easy to do.
The book was worth the wait! The publication quality of the print edition is great. Love the color illustrations. The one thing that I miss is that having bought the print edition, it would be sweet to have an offer to acquire the electronic edition at a reduced price but since Amazon now seems to be handling O’Reilly book sales and probably wants to sell as many Kindle editions as possible, a PDF copy of Hands-On Machine Learning, 2nd Ed., does not seem to be in my future at a bargain price. My review is preliminary – I’ve read bits of the online draft version-and the clarity and superb organization of Géron’s writing convinced me that I wanted a finished copy of the book. My current avocational interest is Reinforcement Learning and Géron gives an excellent overview – to dive deep, one would probably still want to refer to Sutton & Barto’s 2nd Ed. book (available on Amazon or for free online) or David Silver’s excellent 2015 UCL lectures, also available online.. I will slowly work my way through Géron’s book in its entirety but my primary reason for owning the book is as a reference. It makes a great roadmap to the current state of machine learning and, best of all, it makes learning about ML fun!
If you have the budget to only buy one ML book, I would suggest going for this one. It covers most of the field in one book. Get a datacamo subscription too and you can break into the DS career.
Have been advised by many people this is possibly the best book on ML but held off on owning a hard copy as I found it a bit expensive so I grabbed this one roughly 50% off. The level of detail is amazing and everything ML related is nicely explained. It’s nice to see the book was printed in colour which makes the code easier to follow and reproduce. I also liked the layout very much and found it helped to make the book flow – will happily read this cover to cover. The quality of the paper is on thin side but to be fair the content is worth more – I own other similar size ML books printed in black and white that cost more with half the content because it was printed on thick paper. Highly recommended for anyone with an interest in ML.
This book was just released, and I’m only a few chapters into it, but I can already attest that it’s an excellent approach to the topic. It reads like the book I would have written (or perhaps would have liked to have written) if I were to pen a volume on artificial intelligence. It’s a long-overdue addition to the subject, and I’m thrilled to see it executed so well.
This is an excellent book for machine learning, data science and deep learning. The print quality is great, the author’s style of explaining concepts and going into enough depth of the subject is also amazing. I use this as my reference for any machine learning project. It is not just for beginners, it also teaches a lot of advanced concept including creating your custom models, optimisers and loss functions in Tensorflow. It goes from really basic machine learning modelling like linear or logistic regression to advance Deep Learning all the way to generative modelling. It assumes basic prior knowledge in python.
Good book about the machine learning and deep learning. with pratical codes using sklearn, tf2.0. but it is not easy to form a complete solution just from this book. It is worthy read a few times on some chapters to understand better. wonder any alternatives or complementary books…
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