Understanding H2O AutoML Basics
Machine Learning (ML) is the process of building analytical or statistical models using computer systems that learn from historical data and identify patterns in them. These systems then use these patterns and try to make predictive decisions that can provide value to businesses and research alike. However, the sophisticated mathematical knowledge required to implement an ML system that can provide any concrete value has discouraged several people from experimenting with it, leaving tons of undiscovered potential that they could have benefited from.
Automated Machine Learning (AutoML) is one of the latest ML technologies that has accelerated the adoption of ML by organizations of all sizes. It is the process of automating all these complex tasks involved in the ML life cycle. AutoML hides away all these complexities and automates them behind the scenes. This allows anyone to easily implement ML without any hassle and focus more on the results.
In this chapter, we will learn about one such AutoML technology by H2O.ai (https://www.h2o.ai/), which is simply named H2O AutoML. We will provide a brief history of AutoML in general and what problems it solves, as well as a bit about H2O.ai and its H2O AutoML technology. Then, we will code a simple ML implementation using H2O’s AutoML technology and build our first ML model.
By the end of this chapter, you will understand what exactly AutoML is, the company H2O.ai, and its technology H2O AutoML. You will also understand what minimum requirements are needed to use H2O AutoML, as well as how easy it is to train your very first ML model using H2O AutoML without having to understand any complex mathematical rocket science.
In this chapter, we are going to cover the following topics:
- Understanding AutoML and H2O AutoML
- Minimum system requirements to use H2O AutoML
- Installing Java
- Basic implementation of H2O using Python
- Basic implementation of H2O using R
- Training your first ML model using H2O AutoML
