Amazon.com: Customer reviews: Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data

Top positive review

Great for beginner-intermediate learner!

Reviewed in the United States on May 26, 2019

So currently I work as a data scientist, but my main focus is supervised learning and time-series analysis. I got this book to help me brush up on some skills and learn some new ones. This book runs with the suggestion that you already have a good understanding of python, both in terms of general use and data cleansing. Not only does it walk you threw some projects, but if you take time to break down the code and attempt to understand the “why am I using this” instead of “what do I have to replace for my own job” you will learn a lot more.

Personally, I love these kind of books because it shows you multiple ways to test a model, recommends you run multiple models, and even use some underrated features of python. If you have a decent foundation, in python and stats (optional), i highly recommend this book.

9 people found this helpful

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DayDreamer

Very trivial and not worth the cost

Reviewed in the United States on August 28, 2019

Was happy to see a title on unsupervised learning. But my enthusiasm faded as soon as I received the book and started reading. All the examples are pretty trivial. The explanations of topics leading up to examples is where I have problem. The explanations are very shallow, just as good as online documentation of various packages. This book seems to have been hurried to hit the market. Too expensive for the content. Oreilly seems to be churning out too many titles without quality these days to cash in on the trend. Hands-On ML 2nd edition seem to have similar code examples. Hope to see better theory in that that book, just like the first edition which did not have dedicated chapter on Unsupervised Learning.

32 people found this helpful

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DayDreamer

Very trivial and not worth the cost

Reviewed in the United States on August 28, 2019

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32 people found this helpful

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Amazon Customer

Wish I could get a refund

Reviewed in the United States on February 16, 2020

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I had high hopes for this book as it's one of the few that seemed to tackle semi-supervised learning. Its very disappointing. The code is riddled with bugs. There are some beginner mistakes, like scaling the days before splitting it. Much of the content is very very high level. As someone who does quite a few cluster models, the coverage of k-means is so high level that I doubt a newbie could produce a quality model based on this book. The absence of dendograms for hierarchical clustering is bizarre. The use of labeled datasets won't serve newbies well at all. What will they do in the real world without those labels? How else could they validate their model?

Hands on Machine Learning provides better coverage of unsupervised learning. Avoid this book.

20 people found this helpful

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Robert

Great for beginner-intermediate learner!

Reviewed in the United States on May 26, 2019

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9 people found this helpful

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Jonas P. Wei

More like 40% Supervised Learning

Reviewed in the United States on December 8, 2019

Verified Purchase

I really wanted to write a higher star for this book. There just isn't enough meat regarding the topics I was looking for, specifically metric evaluation in a completely unsupervised situation. It's kind of cheating if you throw in a target variable and compare the predicted cluster to the real cluster. Where's the metric evaluation when you don't have a target variable? I haven't seen the section or it's missing. Plus nearly the first hundred pages of the book is going over supervised learning for some reason.

5 people found this helpful

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Ethan

Finally a how-to written for practitioners

Reviewed in the United States on May 2, 2019

Verified Purchase

I've been looking to add more machine learning to my developer toolbox and this book was the absolute best thing I could find on the web. The author is NOT some professor who makes his living writing books, but a man at the edge of the space who has fought for the knowledge and experience in the real world.

I love how the book takes you from ground up implementing real systems in python. Not just unfinished snippets to show you how the m/l packages work (you can read their docs for that!) but he fills in the rest of the bigger picture which is "how do i make something that actually accomplishes a task in the world" your college prof probably has never had to ask that.

At $50 it's a steal, pickup a copy and don't look back.

4 people found this helpful

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Jerome Massot

Who did supervise the redaction of this book?

Reviewed in the United States on February 11, 2022

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As several mentioned before me, this book is a terrible disappointment and I would say a kind of scientific fraud. The technical explanations are poor, several severe mistakes are included, the code is poor. More problematic is the way that the author works : on each data-sciences challenge introduced in the book's chapters, he is accumulating methods that DO NOT WORK and he knows it, as he indicates the poor metrics obtained by his models. In a sense, this book is a succession of modeling failures. So it is a terrible conclusion : writing a good book about Unsupervised Machine Learning needs a lot of ... Supervision from the publisher in order to avoid such catastrophic result.

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Matthew Pupa

Fantastic Book

Reviewed in the United States on April 7, 2019

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As an analyst trying to get more into the world of machine learning and python, I thought this was a great resource. I've been briefly exposed to a lot of the content in this book before, but this book does a great job of breaking concepts down with clear code examples and visualizations. I'd recommend this to any individuals or analytics teams trying to expand their knowledge of machine learning.

7 people found this helpful

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N. Collins

Best-In-Class AI Science

Reviewed in the United States on March 31, 2019

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This is a must-read for anyone interested in building AI applications. The introduction and conclusion are clear and informative, and the main content and code examples are forward thinking. All industry professionals, from data scientists to executives should read this book.

7 people found this helpful

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epictitus

unsupervised examples helps a lot

Reviewed in the United States on November 7, 2019

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I focus on supervised classification in a domain specific area at work.. and would love to find an unsupervised labeling scheme that gets me at least partly to a properly tagged dataset. We don't get the IRIS dataset in the real world. This book had several helpful examples which I tossed in the development hopper as preliminary ideas for better feature engineering. I look forward to future versions with Pytorch and / or Snorkel examples.

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Sam S.

Many Python Programs from this book have errors

Reviewed in the United States on May 2, 2020

Verified Purchase

Number of Python programs from this book are giving errors. Author of this book needs to test this book's programs on Google Coab (Colaboratory) and fix these programs and update the programs on this book's Github web page.
Otherwise, this book is not useful.

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