[](https://packt.link/JGIEY)
3 Days, 20+ AI Experts, 25+ Workshops and Power Talks Code: USD75OFF # Practical Automated Machine Learning Using H2O.ai.
This is the code repository for [Practical Automated Machine Learning Using H2O.ai.](https://www.packtpub.com/product/practical-automated-machine-learning-using-h2o-ai/9781801074520?utm_source=github&utm_medium=repository&utm_campaign=9781801074520), published by Packt.
**Discover the power of automated machine learning, from experimentation through to deployment to production**
## What is this book about?
With the huge amount of data being generated over the internet and the benefits that Machine Learning (ML) predictions bring to businesses, ML implementation has become a low-hanging fruit that everyone is striving for. The complex mathematics behind it, however, can be discouraging for a lot of users. This is where H2O comes in – it automates various repetitive steps,
and this encapsulation helps developers focus on results rather than handling complexities.
This book covers the following exciting features:
* Get to grips with H2O AutoML and learn how to use it
* Explore the H2O Flow Web UI
* Understand how H2O AutoML trains the best models and automates hyperparameter optimization
* Find out how H2O Explainability helps understand model performance
* Explore H2O integration with scikit-learn, the Spring Framework, and Apache Storm
* Discover how to use H2O with Spark using H2O Sparkling Water
If you feel this book is for you, get your [copy](https://www.amazon.com/dp/1800205694) today!
## Instructions and Navigations
All of the code is organized into folders. For example, Chapter03.
The code will look like the following:
```
import h2o
h2o.init()
```
**Following is what you need for this book:**
This book is for engineers and data scientists who want to quickly adopt machine learning into their products without worrying about the internal intricacies of training ML models. If you're someone who wants to incorporate machine learning into your software system but don’t know where to start or don’t have much expertise in the domain of ML, then you’ll find this book useful. Basic knowledge of statistics and programming is beneficial.
Some understanding of ML and Python will be helpful.
With the following software and hardware list you can run all code files present in the book (Chapter 1-13).
### Software and Hardware List
Basic knowledge of statistics and programming is beneficial. Some understanding of ML and Python
will be helpful. You will need Python installed on your computer, preferably with version 3.7 or above,
or R installed on your computer with version 4.0 or above. All code examples have been tested using
Python 3.10 and R 4.1.2 on Windows 10 OS and Ubuntu 22.04.1 LTS. However, they should work
with future version releases too.
| Software require | OS required |
| -------------------------| -----------------------------------|
| Python 3.10 | Windows, Mac OS X, and Linux (Any) |
| R 4.1.2 | |
| H2O 3.36.1.4 | |
| Java 11 | |
| Spark 3.2 | |
| Scala 2.13 | |
| Maven 3.8.6 | |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. [Click here to download it](https://packt.link/IighZ).
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