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Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI 1st Edition

by Darren Cook (Author)
4.3 4.3 out of 5 stars 31 ratings
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Machine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework that’s easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms.

If you’re familiar with R or Python, know a bit of statistics, and have some experience manipulating data, author Darren Cook will take you through H2O basics and help you conduct machine-learning experiments on different sample data sets. You’ll explore several modern machine-learning techniques such as deep learning, random forests, unsupervised learning, and ensemble learning.

  • Learn how to import, manipulate, and export data with H2O
  • Explore key machine-learning concepts, such as cross-validation and validation data sets
  • Work with three diverse data sets, including a regression, a multinomial classification, and a binomial classification
  • Use H2O to analyze each sample data set with four supervised machine-learning algorithms
  • Understand how cluster analysis and other unsupervised machine-learning algorithms work

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  1. ISBN-10
    149196460X
  2. ISBN-13
    978-1491964606
  3. Edition
    1st
  4. Publisher
    O'Reilly Media
  5. Publication date
    January 10, 2017
  6. Language
    English
  7. Dimensions
    7 x 0.63 x 9.19 inches
  8. Print length
    298 pages
  9. See all details
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Editorial Reviews

About the Author

Darren Cook has over 20 years of experience as a software developer, data analyst, and technical director, working on everything from financial trading systems to NLP, data visualization tools, and PR websites for some of the world’s largest brands. He is skilled in a wide range of computer languages, including R, C++, PHP, JavaScript, and Python. He works at QQ Trend, a financial data analysis and data products company.

Product details

  • Publisher ‏ : ‎ O'Reilly Media; 1st edition (January 10, 2017)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 298 pages
  • ISBN-10 ‏ : ‎ 149196460X
  • ISBN-13 ‏ : ‎ 978-1491964606
  • Item Weight ‏ : ‎ 1.06 pounds
  • Dimensions ‏ : ‎ 7 x 0.63 x 9.19 inches
  • Customer Reviews:
    4.3 4.3 out of 5 stars 31 ratings

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Customer reviews

4.3 out of 5 stars
31 global ratings

Top reviews from the United States

Gruyer Francois
5.0 out of 5 stars great first book on H2O machine learning open source framework.
Reviewed in the United States on January 30, 2017
Verified Purchase
This seems to be the very first book on this ML framework (H2O). And is is just great.
The book is crystal clear and extremely comprehensive, very easy to read, with examples you can reproduce easily (datasets are on line in a public Git repo).
It covers a very practical ground on the 4 main algorithms implemented in H2O cluster: RandomForest, GBM, GLM, and last but not least : deep learning...
"Practical" means explanations are strongly grounded on a set of 4 datasets , the author plays with, explaining both their preparation , analysis with H2O (code is both in R and PYTHON), and a great deal of time is spent on very useful considerations on how to 'tune' the various algorithms
to obtain better models, comparing their effectiveness.

All this in very clear style and explanations.

A must have for everyone interested in implementing ML features concretely.

Francois GRUYER
(from Paris, France)
7 people found this helpful
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manuel
5.0 out of 5 stars great machine learning book
Reviewed in the United States on February 24, 2017
This book is an ample introduction of H2O for R and Python practitioners. Those interested in state-of-the-art machine learning and deep learning approaches will enjoy this book completely, whether they are beginners or proficient R and Python users for statistical analysis. The author makes clear descriptions and his explanations are always accessible. His high-quality sense of humour interspersed throughout the text helps maintain the interest in the text as one reads. I would love to read more of this author.
One person found this helpful
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Iván Vitta
4.0 out of 5 stars Concise and practical
Reviewed in the United States on August 9, 2018
Verified Purchase
Practical, just what I needed to start quickly with h2o
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vpatryshev
2.0 out of 5 stars not very practical
Reviewed in the United States on July 13, 2021
Verified Purchase
H2O is not a very serious machine learning company. They have very talented GUI developers, but the backend, including the algorithms and their implementations, mostly suck, and, from a software developer's point of view, are not worth releasing (just POC, not more).
The books is pretty naive. It may be good for salesmen and marketing people, but not for professionals.
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Theophilus Siameh
3.0 out of 5 stars Good for beginners
Reviewed in the United States on February 3, 2018
Book did not teach h2o into details just scratching the surface of h2o. Good for beginners.
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qizheng
1.0 out of 5 stars Hi, I bought two books from Amazon, and ...
Reviewed in the United States on March 16, 2018
Verified Purchase
Hi, I bought two books from Amazon, and some of the plots on the book is blank and I can't stand that, the book name is hands-on machine learning with scikit-learn tensorflow, how can you fix that?
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P.Rotondo
4.0 out of 5 stars very clear and smart, the only problem is h2o (on Win
Reviewed in the United States on May 19, 2017
this book is weel written, very clear and smart, the only problem is h2o (on Win, i don't know on others OS)..--> it's It's almost an ABORTION framework! full of bugs, expecially when try to tuning parameters with search grid...loose your time. ram holding problems, impossoble to work with.
3 people found this helpful
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Matt Res
2.0 out of 5 stars Decent to learn H2O if you know both Python and R
Reviewed in the United States on June 29, 2018
This is by default the best book on H2O, since there aren't others. That said, the author makes a strange choice of splitting the book between Python and R code, particularly considering it is written for an audience that is not fluent in ML modelling. The beginning states that most will be in both languages, but sometimes there will only be Python code; this is not the case. There are huge chunks of R-only. At one point, he writes a bunch of code in R and says the Python equivalent is available on his Git. If it is, then it is named something quite different than the R code. The only way a book like this works is if all the code is available in both languages, and that is not the case.

TL;DR: If you know both Python and R and don't mind splitting work in both while following along, this is a good book. Otherwise, just stick to H2O's booklets and documentation (after learning ML in your language if you don't already).
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Top reviews from other countries

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Angélica
5.0 out of 5 stars Libro
Reviewed in Mexico on June 24, 2024
Verified Purchase
interesante
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Máiron Chaves
5.0 out of 5 stars nice
Reviewed in Brazil on March 24, 2019
Verified Purchase
nice material :)
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Arunangshu Sahu
5.0 out of 5 stars Five Stars
Reviewed in India on May 22, 2017
Verified Purchase
Excellent.. Completely new stuff
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