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Getting Started with

by Niraj Joshi

This course will familiarize you with different recipes of H2O’s Driverless AI. You'll learn to build a fully automated ML pipeline, with built-in feature engineering, feature transformations, automatic visualizations, and inference mechanisms.

What you'll learn

Would you like to build end to end ML Pipelines using H2O Driverless AI? In this course, Getting Started with, you’ll learn to do ML predictive modelling using built-in classification/regression algorithms. First, you’ll explore how to pull in data sets from multiple sources like S3, FileSystem, databases, etc. Next, you’ll discover correlation patterns based on a bunch of visualization methods available within. Finally, you’ll learn how to do feature engineering/transformations, outlier detection, and training based on multiple tuning knobs available like score, interpretability, and accuracy. When you’re finished with this course, you’ll have the skills and knowledge of leveraging capabilities of H2O’s driverless AI in order to build predictive pipelines from scratch to production ready.

About the author

Niraj is a AWS/Azure DevSecOps Cloud Specialist with over a decade of work experience into Data Modeling with Databases like Cassandra, MongoDB, SparkSQL, ElasticSearch and SQL Server. He has over 7 years of work ex into Computer Vision, Artificial Intelligence, DevOps, Machine Learning and Big Data Stack, he has been a consultant to companies like CISCO, ERICSSON, Dynamic Elements and JP Morgan He has excellent data visualization/ analytics skills and quite proficient in languages like Python ,... more

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