Python Machine Learning

I have not finished this book and I just reached chapter 16, but here are my key takeaways for this book: 1. Everything before chapter 13, before the book fully gets into deep learning and TensorFlow, are great. With already some background in python for data analysis (I have also taken the Andrew Ng's Coursera course on Machine Learning), this book supplements my knowledge greatly. The biggest highlight I would say is that it introduces you JUST ENOUGH concepts for you to understand how everything works. In addition, the contents are structured really well, too. If I were to rate this section of the book, I would give 10/10 although it would be better to have some exercises, you can always practice using Kaggle datasets. 2. Since chapter 13 when the book gets into deep learning, things get worse a little bit... The contents are still good in general, however the connections between contents might not be the case. The connections between contents are important for new learners because that helps them to understand how A leads to B and then leads to C. Here, I found the actual TensorFlow documentation a really good material to review along with the book. After reviewing those documentations, coming back to this book allows me to comprehend much more than reading the first time. In addition, if you are not careful enough, the deep learning sections also seems to have accuracy issues with its contents that could confuse people. Even though I have not finished the book, I would give 9/10 for everything I have read for deep learning.

It is rare to find a book that covers the topic in ML to a considerable extent. Further, this books contains practical, working examples of ML that are easy to understand and related theory. Great contribution to the learning content in ML.

I have started to read the first chapter and I have found that the book code just don’t run if you don’t have the correct packages in the Python interpreter. Use the following packages versions for python 3.9.13: NumPy 1.21.2 SciPy 1.7.0 Scikit-learn 1.0 Matplotlib 3.4.3 pandas 1.3.2

I really wanted to like this book, but the printed text is unreadable. Smeared, blurry, and faded beyond legibility. Looks like a washed out photocopy of a page printed on a 1980’s ribbon dot-matrix printer.. but a printer that was partially out of ink. There’s no excuse for this. No reputable publisher would ship material this poor. Update: Was contacted by Amazon rep, and new book with corrected print was shipped free of charge. The replacement text looks great. The book itself is great.. well written, easy to follow, and contains a lot of good information.

I haven't finished reading yet. I am just about halfway through. I like the fact that this book goes into the underlying math and explains concepts very well. The author provides links to his pdf notes where the details are too much of a digression. Rather than using higher-level machine learning libraries like scikit, tensor flow and keras, the author walks through the algorithms in python and numpy. Overall, this book has the right balance between being hands-on with the code and explaining the math. I am happy I got this.

I blazed through this book in the runup to a new job involving ML, and it was the perfect text for the job. Not only does it include information on the code side, it also has substantial theoretical fundamentals. I feel like, for the first time, I really understand what SVMs do, or how decision trees are trained. I'd recommend this volume to anyone who, like me, had substantial experience programming in Python and would like to dive into scikit-learn.

Raschka and Mirjalili's book is required reading for my class in Machine Learning. My students like the clear explanations and illustrations with coding. As a machine learning practitioner, their book is never far away from my computer as reference material. I would recommend that the authors introduce PyTorch and BERT models, among other elements, in their next edition.

1. The quality of printing is bad. 2. I placed the order on Jan 19 2020. When I open the last page of the book, I surprisingly saw "Made in the USA, 19 January 2020", which means they printed the book right after I placed the order. 3. I expect the billing is included with the book. I need it to request reimbursement from my company. But it is missing. Today is Feb 11: I got phone call saying that they provide replacement for free. The new book is on the road. So I am happy to give a better overall rating.

C’est une livre utile !

If you're just starting out this book might be slightly too advanced. It's perfect for junior data scientists who want to refresh their knowledge or touch up on some areas they haven't seen before. In a similar way it would be perfect for people with technical backgrounds such as computer science and mathematics who haven't done any machine learning before but want to.

Quite good textbook arrived in proper condition.

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