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.

_“Distributed Machine Learning and Implementation” .png

c.tar.gz

runlivecode

jupyterNotebook

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

Apply what you have learned about the H2OKMeansEstimator algorithm to analyze the clusters.

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

Challenge: Analyzing Clusters

Problem statement