Expanded

Getting Started with H20.ai

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.
Course info
Rating
(15)
Level
Beginner
Updated
Feb 4, 2021
Duration
1h 26m
Table of contents
Description
Course info
Rating
(15)
Level
Beginner
Updated
Feb 4, 2021
Duration
1h 26m
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Description

Would you like to build end to end ML Pipelines using H2O Driverless AI? In this course, Getting Started with H2O.ai, 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
About the author

Niraj has extensive experience with coding, architecting and consulting experience with data warehousing/ artificial intelligence/ machine learning/ visualization skillsets.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi, everyone. My name is Niraj Joshi. Welcome to my course, Getting started with H2O.ai. I am a cloud machine learning architect. H2O driverless AI is a phenomenal auto ML encyclopedia which can build the most optimized machine learning model in no time. This course will introduce you to perform end‑to‑end machine learning modeling with customized feature enhancements and parameterization. Some of the major topics that we will cover include: automatic visualization, data transformations, bring your own recipe, training the machine learning model, and infer from the machine learning model built. By the end of this course, you will know the art of building end‑to‑end machine learning pipeline with best possible recipes ready for deploying to production. I hope you will join me on this exciting journey to learn auto ML with the Getting Started with H2O.ai course, at Pluralsight