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.

Distributed Machine Learning and Its Implementation with H2O

Availability of labeled data and nature of the task

Complexity of algorithms and model performance

What factors influence the choice of anomaly detection approach?

What factors influence the choice of anomaly detection approach?

Creating a target column indicating anomalies

What is the first step in interpreting anomalies at the dataset level?

What is the first step in interpreting anomalies at the dataset level?

The proportion of normal data points in the dataset

The proportion of features in the dataset

The proportion of missing values in the dataset

The proportion of anomalies within the input dataset

What does the `contamination` parameter in the `H2OIsolationForestEstimator` algorithm control?

What does the <code>contamination</code> parameter in the <code>H2OIsolationForestEstimator</code> algorithm control?

When a record has the highest anomaly score

When a group of trees yields shorter path lengths for specific samples

By comparing records to labeled anomalies

How does the isolation forest model collectively indicate anomalies?

How does the isolation forest model collectively indicate anomalies?

Test your knowledge of H2O anomaly detection modeling concepts.

Introduction to Machine Learning

Supervised Learning: Regression Models with H2O

Supervised Learning: Classification Models with H2O

Unsupervised Learning: Clustering with H2O

Unsupervised Learning: Anomaly Detection with H2O

Closing Notes

Appendix

Quiz: H2O Anomaly Detection Model