In this course you will learn all the core techniques needed to make effective use of H2O for high performance machine learning. Even if you have a little experience of machine learning, by the end of this course you will be able to make highly efficienty machine learning models using a variety of algorithms and tools. Through the whole course we will be focusing on high performance and optimization of Machine Learning in H2O. We focus on solving problems first and foremost! Rather than just teach tools, we teach how to solve problems using H2O in Python.
During the training you will learn how to preprocess data using H2O and Spark, make machine learning models and even more advanced deep learning algorithms to solve business problems at scale. You will also be able to evaluate your models using H2O Flow or Python and choose the best model to suit not just your data but the other business restraints you may be under.
H2O is an open source, easy to use, well-documented and supported predictive analytics platform for data scientists and business analysts who need scalable and fast machine learning. Unlike traditional analytics tools, H2O provides a combination of extraordinary math and high performance parallel processing with unrivaled ease of use. H2O speaks the language of data science with support for R, Python, Scala, Java and a robust REST API. H2O solve today’s most challenging business problems intelligently combining open source technologies, easy-to-use WebUI and data science languages interfaces with support for all common database and file types and Massive scalability. Big Data Munging and Analysis H2O has seen a high surge of users in 2016, and the count only keeps rising. It becomes apparent when one hangs on Kaggle’s Machine Learning competitions.
