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Distributed Machine Learning and Its Implementation with H2O

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level

Advanced

Duration

9h

Lessons

37 Lessons

Category:

Build distributed, fast, and scalable models with H2O, an end-to-end ML framework widely used for fraud risk, churn, advertising, and healthcare analytics.

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This course teaches about a distributed and highly scalable machine learning framework known as H2O-3. H2O has become the go-to solution for organizations seeking to build data-driven and explainable solutions. In this course, you’ll learn about H2O’s versatile machine learning framework, which empowers you with high-performing and interpretable machine learning models.

The H2O framework is compatible with Java, JSON, R, Python, and Scala. You’ll start by covering the concepts of machine learning models. As you progress, you’ll explore H2O’s core strengths: its focus on model interpretability and the efficiency-enhancing AutoML features. You’ll get to implement supervised and unsupervised algorithms and derive insights from them.

This course will equip you with expertise that will advance your career by making them valuable for tackling big data, creating explainable models, and automating ML tasks, opening doors in various industries.

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Distributed Machine Learning and Its Implementation with H2O
Distributed Machine Learning and Its Implementation with H2O
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