Update 11/2/2015:
Finally got the replacement from Amazon. This one is in perfect shape, i.e., no alignment issues at all. Woohoo! Just as promised. For other details, please see my 10/29/15 review below.
Update 10/29/2015:
After having a conversation with the author (see comments), I'm updating my review. Here are some updated remarks on my original comments:
I've been pushed a new copy from Amazon and promised properly aligned pages. I took 2 stars off for the misaligned pages (I'm emailing a picture of these to the author), but I'm giving these stars back because - as per our conversation - my copies were outliers and hopefully the new copy I receive will be properly printed. The three copies the author has are printed properly and mine shouldn't have been different.
I'm also adding a star back because the author explained how the publisher/graphics team didn't do their job right. In conclusion, I'm taking off just one star because the whole 'package' I received isn't how I expected to be, but this isn't a poor reflection on the author. Actually, it's quite the contrary, and I stand by my statement that his content, ideas, thoughts, code and plots do their job well. The breadth of information is immense, and the depth on each topic is enough that I would consider this a great scikit-learn reference.
Original review 10/26/2015 (deprecated):
I didn't want to give this a bad review, trust me. I believe the content is great, the author has done a good job, and he's clearly knowledgeable on the subject matter. The content seems strong, covers a wide-variety of topics, and is a great reference on the Python scikit-learn package. I'm not sure one would be able to learn machine learning from this book alone, primarily because it's actually a reference on scikit-learn, but also because the algorithms are not implemented from scratch and instead covered in concept (and implemented in sklearn with examples here and there). As a reference to sklearn, this book does its job well.
My issue is with the publisher and the editors. EVERY page is printed misaligned, i.e., not printed parallel to the paper. More alarmingly, opposite pages are misaligned in OPPOSITE directions, giving the perception that the pages are misaligned more than they really are! It's horribly distracting while reading the content. I thought it was a one-off thing, but I have seen this in two copies so far. I took off 2 stars for this.
The other issue is with the formatting of the book. Have a look at the contents, for example; there is no line-spacing before or after chapter titles. Going through the contents section feels like I'm reading strange prose with italics, bold and underlined text thrown in here and there. I have to pretty much actively parse the entire contents to find what I'm looking for, or give up in the contents and jump back to the index. It's an annoying, utterly unsatisfying experience and pushes me away from the book. I took a star off here.
The final thing wrong with this book are the illustrations. The charts/plots themselves are okay; they seem to be implemented in matplotlib, our go-to Python plotting library. They do their job, but definitely lack a certain je ne sais quoi. I guess they could've been prettier (think Bokeh or ggplot plotting libraries), but like I said I'm neutral on this. My problem is with the conceptual illustrations. That is, when the author is explaining machine learning or data science concepts, he seems to rely on what looks like MS PowerPoint 2003 or older to build out his diagrams. These plots are so 2003 that I have a hard time gleaning the concepts they're trying to push across. I'm sure the editors could've easily spent some more time recreating these charts in a newer version of PowerPoint (maybe with 2010+ design principles), or even better, in an actual graphic-design platform. Imagine browsing webpages on a Palm Pilot after using an iPhone/Android device for the last decade or so. You're not going to like it, it's going to slow you down, and maybe it'll even impede learning; this is what the illustrations are like. Ugh - I really wish the author, publisher or editor hadn't used the default template on PowerPoint 2003. Puts a bad taste in my mouth every time I think about it. Minus one star for this.
Enjoy fast, FREE delivery, exclusive deals and award-winning movies & TV shows with Prime
Try Prime
and start saving today with Fast, FREE Delivery
Loading your book clubs
There was a problem loading your book clubs. Please try again.
Not in a club? Learn more
Join or create book clubs
Choose books together
Track your books
Bring your club to Amazon Book Clubs, start a new book club and invite your friends to join, or find a club that’s right for you for free.
Download the free Kindle app and start reading Kindle books instantly on your smartphone, tablet, or computer - no Kindle device required. Learn more
Read instantly on your browser with Kindle for Web.
Using your mobile phone camera - scan the code below and download the Kindle app.
Flip to back Flip to front
Follow the Author
Something went wrong. Please try your request again later.
There is a newer edition of this item:
What do customers buy after viewing this item?
Page 1 of 1 Start overPage 1 of 1
About the author
Follow authors to get new release updates, plus improved recommendations.
In my opinion this is a great book to get you up and running with machine learning. It manages to not only cover the basics but also talks about some of the more advanced topics.There are a couple of things that I really liked about this book.1. You learn a lot of things that you can't find online and that are APPLICABLE to the real world. Even if you just want to get into machine learning and use it but don't necessarily want to become a data scientist this is a great buy. Machine Learning can be really useful when put into good use. I for example, after reading the book, was able to quickly write up a python program to predict what time I would wake up based on what time I slept, what day it was etc. As well as having tons of fun playing with data from http://archive.ics.uci.edu/ml/ .2. Although this book is focusing on python the math that you need to implement the algorithms are all there. What's great about that is that I was able to "Translate" most of the examples from the book to C++ code without much hustle. Not only that but the math behind these algorithms made a lot more sense after reading this book. So even if you don't necessarily want to use python but want to gain intuition over how these algorithms work this book will also come in handy.3. This book isn't just about Machine Learning algorithms. It actually talks quite a bit about preparing and getting good data in general. Which is crucial for every data scientist since almost 80% of your job is getting good data. And another 20% finding a good model and training it.Overall I would say that this book helped me and that I learnt a bunch of new things.If the above didn't convince you (Along with other reviews) here are some small details that made the reading of this book a joyful experience.- I felt that reading the book was actually really fun and motivating since at every chapter there were several examples of applying the theory taught. Which motivated me to move on and read more.- Although this may not seem as important. I have to say that the font of the book as well as the tone of the writing made the reading of the book really comfortable and joyful. I didn't feel that I was getting tired and was easy for me to pick it up where I left off.I have to say though that there where some typos here and there (I thing I found 2-3 in total as well as 2 pictures where swapped) but they were easy to spot so it wasn't that big of a problem.Reasons why you shouldn't buy this book:Unless you are a Machine Learning expert and you look into the deeper insights and more advanced stuff in Machine Learning you shouldn't be looking into buying this book since most of the stuff taught is already known to you. (Although I doubt that you would be looking through this reviews thinking whether to buy it or not in this case).I have also included some pictures.Great Book. Highly Recommend it!
Images in this review
Top reviews from the United States
There was a problem filtering reviews right now. Please try again later.
Reviewed in the United States 🇺🇸 on October 27, 2015
Reviewed in the United States 🇺🇸 on October 10, 2016
In my opinion this is a great book to get you up and running with machine learning. It manages to not only cover the basics but also talks about some of the more advanced topics.
There are a couple of things that I really liked about this book.
1. You learn a lot of things that you can't find online and that are APPLICABLE to the real world. Even if you just want to get into machine learning and use it but don't necessarily want to become a data scientist this is a great buy. Machine Learning can be really useful when put into good use. I for example, after reading the book, was able to quickly write up a python program to predict what time I would wake up based on what time I slept, what day it was etc. As well as having tons of fun playing with data from http://archive.ics.uci.edu/ml/ .
2. Although this book is focusing on python the math that you need to implement the algorithms are all there. What's great about that is that I was able to "Translate" most of the examples from the book to C++ code without much hustle. Not only that but the math behind these algorithms made a lot more sense after reading this book. So even if you don't necessarily want to use python but want to gain intuition over how these algorithms work this book will also come in handy.
3. This book isn't just about Machine Learning algorithms. It actually talks quite a bit about preparing and getting good data in general. Which is crucial for every data scientist since almost 80% of your job is getting good data. And another 20% finding a good model and training it.
Overall I would say that this book helped me and that I learnt a bunch of new things.
If the above didn't convince you (Along with other reviews) here are some small details that made the reading of this book a joyful experience.
- I felt that reading the book was actually really fun and motivating since at every chapter there were several examples of applying the theory taught. Which motivated me to move on and read more.
- Although this may not seem as important. I have to say that the font of the book as well as the tone of the writing made the reading of the book really comfortable and joyful. I didn't feel that I was getting tired and was easy for me to pick it up where I left off.
I have to say though that there where some typos here and there (I thing I found 2-3 in total as well as 2 pictures where swapped) but they were easy to spot so it wasn't that big of a problem.
Reasons why you shouldn't buy this book:
Unless you are a Machine Learning expert and you look into the deeper insights and more advanced stuff in Machine Learning you shouldn't be looking into buying this book since most of the stuff taught is already known to you. (Although I doubt that you would be looking through this reviews thinking whether to buy it or not in this case).
I have also included some pictures.
Great Book. Highly Recommend it!
There are a couple of things that I really liked about this book.
1. You learn a lot of things that you can't find online and that are APPLICABLE to the real world. Even if you just want to get into machine learning and use it but don't necessarily want to become a data scientist this is a great buy. Machine Learning can be really useful when put into good use. I for example, after reading the book, was able to quickly write up a python program to predict what time I would wake up based on what time I slept, what day it was etc. As well as having tons of fun playing with data from http://archive.ics.uci.edu/ml/ .
2. Although this book is focusing on python the math that you need to implement the algorithms are all there. What's great about that is that I was able to "Translate" most of the examples from the book to C++ code without much hustle. Not only that but the math behind these algorithms made a lot more sense after reading this book. So even if you don't necessarily want to use python but want to gain intuition over how these algorithms work this book will also come in handy.
3. This book isn't just about Machine Learning algorithms. It actually talks quite a bit about preparing and getting good data in general. Which is crucial for every data scientist since almost 80% of your job is getting good data. And another 20% finding a good model and training it.
Overall I would say that this book helped me and that I learnt a bunch of new things.
If the above didn't convince you (Along with other reviews) here are some small details that made the reading of this book a joyful experience.
- I felt that reading the book was actually really fun and motivating since at every chapter there were several examples of applying the theory taught. Which motivated me to move on and read more.
- Although this may not seem as important. I have to say that the font of the book as well as the tone of the writing made the reading of the book really comfortable and joyful. I didn't feel that I was getting tired and was easy for me to pick it up where I left off.
I have to say though that there where some typos here and there (I thing I found 2-3 in total as well as 2 pictures where swapped) but they were easy to spot so it wasn't that big of a problem.
Reasons why you shouldn't buy this book:
Unless you are a Machine Learning expert and you look into the deeper insights and more advanced stuff in Machine Learning you shouldn't be looking into buying this book since most of the stuff taught is already known to you. (Although I doubt that you would be looking through this reviews thinking whether to buy it or not in this case).
I have also included some pictures.
Great Book. Highly Recommend it!
Images in this review
Top reviews from other countries
Jules
Well simply excellent. Just the right mix of brief maths overview ...
Reviewed in the United Kingdom 🇬🇧 on December 13, 2017Daniel Alexandru Radu
Good but not for absolute beginners
Reviewed in the United Kingdom 🇬🇧 on February 14, 2017
