Gain insights into H2O-3's scalable framework and explore model interpretability, AutoML features, and algorithm implementation. Discover how to derive insights and tackle big data for explainable ML solutions.

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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

It helps to visualize the distribution of the target variable in the dataset.

It trains multiple models with different hyperparameters and selects the best one based on a given metric.

It evaluates the impact of feature engineering on a model.

It determines the optimal number of trees and their depth in a GBM model.

What is the purpose of grid search in H2O?

What is the purpose of grid search in H2O?

Which parameter is optional for setting up the grid search with H2O?

Which parameter is optional for setting up the grid search with H2O?

It sets the maximum depth of the trees in the model.

It specifies the number of trees in the model.

It prevents overfitting by stopping the algorithm early.

What is the purpose of the `stopping_metric` parameter in the H2O GBM algorithm?

What is the purpose of the <code>stopping_metric</code> parameter in the H2O GBM algorithm?

The final training and validation metrics for a trained model

The feature importance scores for all the variables in the dataset

The grid search results for the hyperparameters of a model

The history of training and validation metrics for each iteration of a model

What does the `scoring_history()` method return in H2O?

What does the <code>scoring_history()</code> method return in H2O?

If `col_sample_rate_per_tree` is set to `0.9` and `col_sample_rate` is set to `0.6` in an H2O GBM model, what is the percentage of columns that will be considered at any given node to split?

If <code>col_sample_rate_per_tree</code> is set to <code>0.9</code> and <code>col_sample_rate</code> is set to <code>0.6</code> in an H2O GBM model, what is the percentage of columns that will be considered at any given node to split?

Test your knowledge and understanding of H2O classification 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 Classification Concepts