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Practical Machine Learning with H2O
Get Free Ebook Practical Machine Learning with H2O
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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.
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Product details
Paperback: 300 pages
Publisher: O'Reilly Media; 1 edition (December 16, 2016)
Language: English
ISBN-10: 9781491964606
ISBN-13: 978-1491964606
ASIN: 149196460X
Product Dimensions:
7 x 0.7 x 9.2 inches
Shipping Weight: 15.2 ounces (View shipping rates and policies)
Average Customer Review:
3.3 out of 5 stars
7 customer reviews
Amazon Best Sellers Rank:
#554,852 in Books (See Top 100 in Books)
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 algorithmsto 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)
Practical, just what I needed to start quickly with h2o
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).
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.
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?
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.
Book did not teach h2o into details just scratching the surface of h2o. Good for beginners.
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